Upcoming HI surveys will deliver such large datasets that automated
processing using the full 3-D information to find and characterize HI objects
is unavoidable. Full 3-D visualization is an essential tool for enabling
qualitative and quantitative inspection and analysis of the 3-D data, which is
often complex in nature. Here we present $\tt{SlicerAstro}$, an open-source
extension of 3DSlicer, a multi-platform open source software package for
visualization and medical image processing, which we developed for the
inspection and analysis of HI spectral line data. We describe its initial
capabilities, including 3-D filtering, 3-D selection and comparative modelling

Punzo, D.

Roerdink, J. B. T. M.

van der Hulst, J. M.

English

arXiv

University of Groningen Research Database

3-D interactive visualisation tools for Hi spectral line imaging

Upcoming HI surveys will deliver such large datasets that automated processing using the full 3-D information to find and characterize HI objects is unavoidable. Full 3-D visualization is an essential tool for enabling qualitative and quantitative inspection and analysis of the 3-D data, which is often complex in nature. Here we present SlicerAstro, an open-source extension of 3DSlicer, a multi-platform open source software package for visualization and medical image processing, which we developed for the inspection and analysis of HI spectral line data. We describe its initial capabilities, including 3-D filtering, 3-D selection and comparative modelling

Proceedings - University of Groningen

   University of Groningen3-D interactive visualisation tools for Hi spectral line imagingvan der Hulst, J. M.; Punzo, D.; Roerdink, J. B. T. M.Published in:Proceedings of the International Astronomical Union, IAU SymposiumDOI:10.1017/S174392131700134XIMPORTANT NOTE: You are advised to consult the publisher's version (publisher's PDF) if you wish to cite fromit. Please check the document version below.Document VersionPublisher's PDF, also known as Version of recordPublication date:2016Link to publication in University of Groningen/UMCG research databaseCitation for published version (APA):van der Hulst, J. M., Punzo, D., & Roerdink, J. B. T. M. (2016). 3-D interactive visualisation tools for Hispectral line imaging. Proceedings of the International Astronomical Union, IAU Symposium, 325, 305-310.https://doi.org/10.1017/S174392131700134XCopyrightOther than for strictly personal use, it is not permitted to download or to forward/distribute the text or part of it without the consent of theauthor(s) and/or copyright holder(s), unless the work is under an open content license (like Creative Commons).The publication may also be distributed here under the terms of Article 25fa of the Dutch Copyright Act, indicated by the “Taverne” license.More information can be found on the University of Groningen website: https://www.rug.nl/library/open-access/self-archiving-pure/taverne-amendment.Take-down policyIf you believe that this document breaches copyright please contact us providing details, and we will remove access to the work immediatelyand investigate your claim.Downloaded from the University of Groningen/UMCG research database (Pure): http://www.rug.nl/research/portal. For technical reasons thenumber of authors shown on this cover page is limited to 10 maximum.Download date: 31-10-2023AstroinformaticsProceedings IAU Symposium No. 325, 2016M. Brescia, S.G. Djorgovski, E. Feigelson,G. Longo & S. Cavuoti, eds.c© International Astronomical Union 2017doi:10.1017/S174392131700134X3-D interactive visualisation tools for H Ispectral line imagingJ. M. van der Hulst, D. Punzo1 and J. B. T. M. Roerdink21Kapteyn Astronomical Institute, University of Groningen,Landleven 12, NL-9747AD, Groningen, the Netherlandsemail: j.m.van.der.hulst@rug.nlpunzo@astro.rug.nl2 Johann Bernoulli Institute for Mathematics and Computer Science, University of Groningen,Nijenborgh 9, NL-9747AG Groningen, the Netherlandsemail: j.b.t.m.roerdink@rug.nlAbstract. Upcoming H I surveys will deliver such large datasets that automated processingusing the full 3-D information to find and characterize H I objects is unavoidable. Full 3-Dvisualization is an essential tool for enabling qualitative and quantitative inspection and analysisof the 3-D data, which is often complex in nature. Here we present SlicerAstro, an open-sourceextension of 3DSlicer, a multi-platform open source software package for visualization andmedical image processing, which we developed for the inspection and analysis of H I spectral linedata. We describe its initial capabilities, including 3-D filtering, 3-D selection and comparativemodelling.Keywords. techniques: image processing; methods: data analysis; galaxies: structure, evolution,interactions, ISM1. IntroductionUpcoming H I surveys with instruments such as ASKAP (Australian Square KilometreArray Pathfinder, Johnston et al. 2008) and Apertif on the WSRT (Westerbork SynthesisRadio Telescope, Verheijen et al. 2009, Oosterloo et al. 2010) will provide large dataflows, in the regime of a few TB per day. Automation of the calibrating and imaging isimperative, and so is the automation of finding objects in the noisy H I data cubes. Theexpectation is that an H I survey with each of these instruments is capable of deliveringof the order of 100 objects per day (Duffy et al. 2012). Automated source finders suchas Duchamp (Whiting 2012) or SoFiA (Serra et al. 2015) will locate the detected objects.Yet in many cases, in particular when the objects are well resolved, further inspection isrequired to fully explore the characteristics of these object and interpret the subtle signsin the H I data of the various mechanisms of gas accretion and removal in galaxies (foran extended review of these see Sancisi et al. 2008).In the early 90’s it was realised that viewers for radio-astronomical spectral line datawould benefit greatly from advanced 3-D visualization software (Norris 1994). It was veryclear already then that full a 3-D approach would greatly help understanding of the 3-Dstructure of the radio data. The type of 2-D data slicing commonly used (i.e., channelmovies), and implemented in packages such as karma (Gooch 1996), is useful but limitedin that it misses the capability to get a full 3-D view of the data. Recently, both Hassan& Fluke (2011) and Koribalski (2012) pointed this out and and described need for 3-Dvisualisation capabilities in the context of upcoming H I surveys.305https://www.cambridge.org/core/terms. https://doi.org/10.1017/S174392131700134XDownloaded from https://www.cambridge.org/core. University of Groningen, on 18 Jan 2018 at 08:30:54, subject to the Cambridge Core terms of use, available at306 J. M. van der Hulst, D. Punzo & J. B. T. M. RoerdinkPunzo et al. (2015) discussed the requirements for a fully interactive visualization tool:coupled 1-D/2-D/3-D visualization, quantitative and comparative capabilities, combinedwith supervised semi-automated analysis. In addition the source code must be open, mod-ular, well documented, and well maintained. Punzo et al. (2015) reviewed several existingpackages and decided to develop a module for 3DSlicer (URL: https://www.slicer.org/)which adds the following characteristics to the already large number of capabilities ofthe current 3DSlicer system: (i) the ability to read FITS (Flexible Image TransportSystem, Wells et al. 1981) files and handle the astronomical world coordinates system(WCS, Calabretta & Greisen 2002, Greisen & Calabretta 2002, and Greisen et al. 2006),(ii) coupled 1-D, 2-D and 3-D display of the data and fast rendering, (iii) semi-automatedfiltering methods to recover faint emission using the information in all three dimensions(Punzo et al. 2016), (iv) 3-D selection and (v) use of 3-D modelling to characterise andselect the regularly rotating H I disks in galaxies. This paper gives a brief overview of thecapabilities of SlicerAstro. For a detailed description see Punzo et al. (2015), Punzoet al. (2016) and Punzo et al. (2017, in preparation).2. SlicerAstroSlicerAstro (URL: https://github.com/Punzo/SlicerAstro) is an open-source exten-sion of 3DSlicer, provided via its built-in extension manager. All functionality withinSlicerAstro is geared toward handling 3-D radio astronomical data, but makes use of allbuilt in features of the core software of 3DSlicer, fully exploiting the fast 3-D rendering,linked display capabilities and segmentation or region of interest selection. It will be ableto handle 3-D data cubes of op to 109 voxels on modern desktop machines which areusually with rather fast GPU’s. The present implementation of SlicerAstro providesthe following features that are of great interest for the exploration of radio astronomicalspectral line (in particular H I) data:(a) visualization of astronomical data cubes using the FITS data format;(b) active use of proper astronomical world coordinates and physical units;(c) interactive (semi)automatic smoothing in all three dimensions;(d) interactive 3-D selection of H I sources;(e) interactive H I data modelling coupled to visualization.Figure 1 shows the basic layout of SlicerAstro in the 3DSlicer environment. Theleft panel includes widgets for changing the colour transfer function for 2-D displays andfor changing the properties of the volume rendering, In addition it includes interfacewidgets to control the display proprieties (e.g., user interaction to rotate the 3-D view)and a window for displaying the properties of a data probe coupled to the cursor in thelinked 2-D views. The rest of the display in Figure 1 can be used to display the data in anumber of user specified ways, ranging from a single 3D view to a combination of linkedviews as presented here. In Figure 1 the arrangement of views consists of two linked 3Dviews of two different versions of the same data set (the original data to the left and thefiltered data to the right) at the top and three linked 2D views at the bottom displayingthe original data in three slices: right ascension versus declination, velocity versus rightascension and velocity versus declination. At the top of these three 2D displays there isa slide bar which allows moving through the third coordinate, i.e. velocity, declinationand right ascension respectively.https://www.cambridge.org/core/terms. https://doi.org/10.1017/S174392131700134XDownloaded from https://www.cambridge.org/core. University of Groningen, on 18 Jan 2018 at 08:30:54, subject to the Cambridge Core terms of use, available at3-D visualisation of H I data 307Figure 1. Screenshot of a full view of SlicerAstro in 3DSlicer. The left panel contains widgetswith various control functions described in the text, the right panel is a 5 frame layout showinglinked 3D views of two data sets at the top and three linked 2D views of the left data set atthe bottom. Additional labeling indicates the location of the extension manager control button(for adding packages) and the probe region (lower left) where the astronomical coordinates aredisplayed of the cursor position in the bottom 2D displays.2.1. FilteringIn order to find and enhance faint and extended features in H I data one usually lowersthe resolution in right ascension, declination and/or velocity to increase the signal-to-noise. The optimal shape of the smoothing kernel is of course the one that follows the3D structure of the faint features in the data such that it is just resolved in each of thethree dimensions. The exact shape is, however, not known a priory, so that in practiceone often uses a set of different kernels and then inspects the results to make a finaldecision about which one suits best.Punzo et al. (2016) have investigated this problem and showed that there is an optimumfiltering algorithm that works very effectively on H I data. This filter is an adaptive filter,based on the intensity-gradient driven filter of Perona & Malik (1990), which appearedto be quite effective on H I data with well chosen default parameters. This filter, alsoknown as anisotropic diffusion, does preserve the original resolution for regions wherethe signal-to-noise is high, but adapts to lower resolutions in regions where the signal-to-noise becomes very low but where there still is coherence in the data on larger spatialand/or velocity scales. This method has been implemented in SlicerAstro, in additionto the general Gaussian smoothing. The left 3D panel in Figure 1 shows a version ofthe high resolution data displayed at the left after application of this intensity-gradientdriven filter.2.2. 3-D data selectionHaving an optimized 3-D selection technique is still a problem that has not been solvedsatisfactorily, not only in astronomy, but also not in medical and other visualizationapplications. Moreover, the optimal selection technique highly depends on the naturehttps://www.cambridge.org/core/terms. https://doi.org/10.1017/S174392131700134XDownloaded from https://www.cambridge.org/core. University of Groningen, on 18 Jan 2018 at 08:30:54, subject to the Cambridge Core terms of use, available at308 J. M. van der Hulst, D. Punzo & J. B. T. M. RoerdinkFigure 2. Screenshot of the CloudLasso selection in SlicerAstro. The selection as drawn bythe user in the top right panel with the filtered data is indicated by the yellow line. The selectionis simultaneously displayed in the top left panel displaying the original, unfiltered data. For thefinal selection see Figure 3Figure 3. Screenshot showing three selections, highlighted in blue, ochre and red respectively,with the CloudLasso selection tool. The blue segmentation shows the selection in the CloudLassoregion indicated in Figure 2. The ochre and red segmentations are separate selections of thesecond galaxy and the H I structure between the galaxies respectively. Both the two top 3-Ddisplays and the bottom three 2-D displays show all three selections simultaneously. They canbe switched on or off separately.https://www.cambridge.org/core/terms. https://doi.org/10.1017/S174392131700134XDownloaded from https://www.cambridge.org/core. University of Groningen, on 18 Jan 2018 at 08:30:54, subject to the Cambridge Core terms of use, available at3-D visualisation of H I data 309of the data, i.e. the complexity of the 3-D data structure. Requirements for a 3-D se-lection tool are interactivity and minimal number of user-operations for achieving theselection (i.e. user-friendliness). Yu et al. (2012a) and Yu et al. (2012b) provide a reviewof the state-of-the-art 3-D selection algorithms. In SlicerAstro we implemented theCloudLasso technique Yu et al. (2012) for 3-D selection. The CloudLasso selection usesthe Marching Cubes (MC) algorithm (Wyvill et al. 1986, Lorensen & Cline 1987) for theidentification of regions of voxels with signal inside a user-drawn lasso, i. e. CloudLassois a lasso-constrained Marching Cubes method. The MC method allows spatial selectionof individual structures with a specified signal-to-noise ratio (usually  3) within a lassoregion, even if these lie visually one behind one another, without including the noisyregions in between. A detailed description of the CloudLasso selection can be found inPunzo et al. (2017, in preparation).Figure 2 shows a region selected with the CloudLasso. The selection in Figure 2 hasbeen done on the filtered version of the data (top right panel) and is indicated by theclosed yellow line. In this example the selection is automatically applied to the originaldata shown in the top left panel. The actual selection using the MC algorithm is shownin Figure 3 as the region highlighted in blue. The selection (defined as a segmentationstructure in 3DSlicer) is also shown in the three lower slices in Figure 3, highlightedin the same blue colour. In addition Figure 3 shows two other selections made withthe CloudLasso: a red selection around the faint H I bridge connecting the two galaxiesin this example and a ochre selection highlighting the second galaxy. Also these twoselections are shown in the three slices at the bottom. The CloudLasso implementationallows interactive modification of these selections.The selections are saved in 3DSlicer as segmentation structures, but can be exportedas FITS files in the form of masks. All voxels in such a FITS file are zeros, except forthe ones within a segmentation structure. These are numbered 1, 2, 3, etc. following thenumbering of the segmentations. Conversely a FITS file with masks, produced by anotherprogramme, e.g. SoFiA (Serra et al. 2015), can be read into SlicerAstro, be displayedand modified using the CloudLasso mechanism.2.3. Model aided selectionRather than selecting regions by hand, one can use a model representing the data tohighlight the parts of the 3-D structure that fit the model well and separate those fromthe regions in the 3-D data volume that do not fit the data. A simple example is thefollowing (see Figure 5 in Punzo et al. 2015): one can use a classical tilted ring modelto describe the regularly rotating H I disk of an observed galaxy and use this model tohighlight all parts of the data that fit the model well. All data that does not fit the model,i.e. deviates from regular rotation, can then easily be viewed as it will not be highlighted,i.e. have a contrasting colour. The full 3-D rendering capabilities of SlicerAstro thenallow the user to easily inspect the full 3-D structure of the H I that exhibits anomalousvelocities.This capability is being implemented in SlicerAstro as well, using the automated3-D fitting routine 3D Barolo (Di Teodoro & Fraternali 2015) for fitting the regularlyrotating H I disks in galaxies. This implementation provides both a (partly interactive)modelling capability and allows separating the H I with regular kinematics from the H Iwith anomalous kinematics in the data cubes of observed galaxies. The data used for themodel fitting can be preselected using the CloudLasso procedure described above.In this overview we have presented a new extension, SlicerAstro, that has beenadded to the existing 3DSlicer platform, developed originally for scientific visualisa-tion of medical data. SlicerAstro is not only capable of reading FITS format data,https://www.cambridge.org/core/terms. https://doi.org/10.1017/S174392131700134XDownloaded from https://www.cambridge.org/core. University of Groningen, on 18 Jan 2018 at 08:30:54, subject to the Cambridge Core terms of use, available at310 J. M. van der Hulst, D. Punzo & J. B. T. M. Roerdinkbut also understand and handles astronomical world coordinates and physical units.The first few capabilities implemented in SlicerAstro and described here are filtering,3-D selection and model aided exploration of H I data. The system is still under de-velopment and we expect to add more features in the future. User feedback is greatlyappreciated and can be directed to the first two authors of this paper or be posted onhttps://github.com/Punzo/SlicerAstro.3. AcknowledgmentsD. Punzo and J.M van der Hulst gratefully acknowledge support from the European Re-search Council under the European Union’s Seventh Framework Programme (FP/2007-2013)/ERC Grant Agreement nr. 291531.ReferencesCalabretta, M. R. & Greisen, E. W. 2002, A&A, 395, 1077Duffy, A. R., Meyer, M. J., Staveley-Smith, L., et al. 2012, MNRAS, 426, 3385Di Teodoro, E. M. & Fraternali, F. 2015, MNRAS 451, 3021Gooch, R. 1996, Astronomical Data Analysis Software and Systems V, 101, 80Greisen, E. W. & Calabretta, M. R. 2002, A&A, 395, 1061Greisen, E. W., Calabretta, M. R., Valdes, F. G., & Allen, S. L. 2006, A&A, 446, 747Hassan, A. & Fluke, C. J. 2011, Publications of the Astronomical Society of Australia, 28, 150Johnston, S., Taylor, R., Bailes, M., et al. 2008, Experimental Astronomy, 22, 151Koribalski, B. S. 2012, Publications of the Astronomical Society of Australia, 29, 359Lorensen, W. E. & Cline, H. E. 1987, SIGGRAPH Comput. Graph. vol. 21, nr. 4, 163Norris, R. P. 1994, Astronomical Data Analysis Software and Systems III, 61, 51Oosterloo, T., Verheijen, M., & van Cappellen, W. 2010, ISKAF2010 Science Meeting, 43Perona, P & Malik, J. 1990, IEEE, 12, 629Punzo, D., van der Hulst, J. M., & Roerdink, J. B. T. M. 2016, Astronomy and Computing, 17,163Punzo, D., van der Hulst, J. M., Roerdink, J. B. T. M., et al. 2015, Astronomy and Computing,12, 86Sancisi, R., Fraternali, F., Oosterloo, T., & van der Hulst, T. 2008, A&A Review, 15, 189Serra, P., Westmeier, T., Giese, N., et al. 2015, MNRAS, 448, 1922Verheijen, M., Oosterloo, T., Heald, G., & van Cappellen, W. 2009, Panoramic Radio Astronomy:Wide-field 1-2 GHz Research on Galaxy Evolution, 10Wells, D. C., Greisen, E. W., & Harten, R. H. 1981, A&A Suppl., 44, 363Whiting, M. T. 2012, MNRAS, 421, 3242Wyvill, G., McPheeters, C., & Wyvill, B. 1986, The Visual Computer, vol. 2, nr. 4, 227Yu, L., Efstatiou, K., Isenberg, P., & Isenberg, T. 2012, IEEE Transactions on Visualizationand Computer Graphics, vol. 18, nr. 12, 2245Yu, L., Efstatiou, K., Isenberg, P., & Isenberg, T. 2012, IEEE Transactions on Visualizationand Computer Graphics, vol. 22, nr. 1, 1077https://www.cambridge.org/core/terms. https://doi.org/10.1017/S174392131700134XDownloaded from https://www.cambridge.org/core. University of Groningen, on 18 Jan 2018 at 08:30:54, subject to the Cambridge Core terms of use, available at

   University of Groningen3-D interactive visualisation tools for Hi spectral line imagingvan der Hulst, J. M.; Punzo, D.; Roerdink, J. B. T. M.Published in:Proceedings of the International Astronomical Union, IAU SymposiumDOI:10.1017/S174392131700134XIMPORTANT NOTE: You are advised to consult the publisher's version (publisher's PDF) if you wish to cite fromit. Please check the document version below.Document VersionPublisher's PDF, also known as Version of recordPublication date:2016Link to publication in University of Groningen/UMCG research databaseCitation for published version (APA):van der Hulst, J. M., Punzo, D., & Roerdink, J. B. T. M. (2016). 3-D interactive visualisation tools for Hispectral line imaging. Proceedings of the International Astronomical Union, IAU Symposium, 325, 305-310.https://doi.org/10.1017/S174392131700134XCopyrightOther than for strictly personal use, it is not permitted to download or to forward/distribute the text or part of it without the consent of theauthor(s) and/or copyright holder(s), unless the work is under an open content license (like Creative Commons).The publication may also be distributed here under the terms of Article 25fa of the Dutch Copyright Act, indicated by the “Taverne” license.More information can be found on the University of Groningen website: https://www.rug.nl/library/open-access/self-archiving-pure/taverne-amendment.Take-down policyIf you believe that this document breaches copyright please contact us providing details, and we will remove access to the work immediatelyand investigate your claim.Downloaded from the University of Groningen/UMCG research database (Pure): http://www.rug.nl/research/portal. For technical reasons thenumber of authors shown on this cover page is limited to 10 maximum.Download date: 12-10-2022AstroinformaticsProceedings IAU Symposium No. 325, 2016M. Brescia, S.G. Djorgovski, E. Feigelson,G. Longo & S. Cavuoti, eds.c© International Astronomical Union 2017doi:10.1017/S174392131700134X3-D interactive visualisation tools for H Ispectral line imagingJ. M. van der Hulst, D. Punzo1 and J. B. T. M. Roerdink21Kapteyn Astronomical Institute, University of Groningen,Landleven 12, NL-9747AD, Groningen, the Netherlandsemail: j.m.van.der.hulst@rug.nlpunzo@astro.rug.nl2 Johann Bernoulli Institute for Mathematics and Computer Science, University of Groningen,Nijenborgh 9, NL-9747AG Groningen, the Netherlandsemail: j.b.t.m.roerdink@rug.nlAbstract. Upcoming H I surveys will deliver such large datasets that automated processingusing the full 3-D information to find and characterize H I objects is unavoidable. Full 3-Dvisualization is an essential tool for enabling qualitative and quantitative inspection and analysisof the 3-D data, which is often complex in nature. Here we present SlicerAstro, an open-sourceextension of 3DSlicer, a multi-platform open source software package for visualization andmedical image processing, which we developed for the inspection and analysis of H I spectral linedata. We describe its initial capabilities, including 3-D filtering, 3-D selection and comparativemodelling.Keywords. techniques: image processing; methods: data analysis; galaxies: structure, evolution,interactions, ISM1. IntroductionUpcoming H I surveys with instruments such as ASKAP (Australian Square KilometreArray Pathfinder, Johnston et al. 2008) and Apertif on the WSRT (Westerbork SynthesisRadio Telescope, Verheijen et al. 2009, Oosterloo et al. 2010) will provide large dataflows, in the regime of a few TB per day. Automation of the calibrating and imaging isimperative, and so is the automation of finding objects in the noisy H I data cubes. Theexpectation is that an H I survey with each of these instruments is capable of deliveringof the order of 100 objects per day (Duffy et al. 2012). Automated source finders suchas Duchamp (Whiting 2012) or SoFiA (Serra et al. 2015) will locate the detected objects.Yet in many cases, in particular when the objects are well resolved, further inspection isrequired to fully explore the characteristics of these object and interpret the subtle signsin the H I data of the various mechanisms of gas accretion and removal in galaxies (foran extended review of these see Sancisi et al. 2008).In the early 90’s it was realised that viewers for radio-astronomical spectral line datawould benefit greatly from advanced 3-D visualization software (Norris 1994). It was veryclear already then that full a 3-D approach would greatly help understanding of the 3-Dstructure of the radio data. The type of 2-D data slicing commonly used (i.e., channelmovies), and implemented in packages such as karma (Gooch 1996), is useful but limitedin that it misses the capability to get a full 3-D view of the data. Recently, both Hassan& Fluke (2011) and Koribalski (2012) pointed this out and and described need for 3-Dvisualisation capabilities in the context of upcoming H I surveys.305https://www.cambridge.org/core/terms. https://doi.org/10.1017/S174392131700134XDownloaded from https://www.cambridge.org/core. University of Groningen, on 18 Jan 2018 at 08:30:54, subject to the Cambridge Core terms of use, available at306 J. M. van der Hulst, D. Punzo & J. B. T. M. RoerdinkPunzo et al. (2015) discussed the requirements for a fully interactive visualization tool:coupled 1-D/2-D/3-D visualization, quantitative and comparative capabilities, combinedwith supervised semi-automated analysis. In addition the source code must be open, mod-ular, well documented, and well maintained. Punzo et al. (2015) reviewed several existingpackages and decided to develop a module for 3DSlicer (URL: https://www.slicer.org/)which adds the following characteristics to the already large number of capabilities ofthe current 3DSlicer system: (i) the ability to read FITS (Flexible Image TransportSystem, Wells et al. 1981) files and handle the astronomical world coordinates system(WCS, Calabretta & Greisen 2002, Greisen & Calabretta 2002, and Greisen et al. 2006),(ii) coupled 1-D, 2-D and 3-D display of the data and fast rendering, (iii) semi-automatedfiltering methods to recover faint emission using the information in all three dimensions(Punzo et al. 2016), (iv) 3-D selection and (v) use of 3-D modelling to characterise andselect the regularly rotating H I disks in galaxies. This paper gives a brief overview of thecapabilities of SlicerAstro. For a detailed description see Punzo et al. (2015), Punzoet al. (2016) and Punzo et al. (2017, in preparation).2. SlicerAstroSlicerAstro (URL: https://github.com/Punzo/SlicerAstro) is an open-source exten-sion of 3DSlicer, provided via its built-in extension manager. All functionality withinSlicerAstro is geared toward handling 3-D radio astronomical data, but makes use of allbuilt in features of the core software of 3DSlicer, fully exploiting the fast 3-D rendering,linked display capabilities and segmentation or region of interest selection. It will be ableto handle 3-D data cubes of op to 109 voxels on modern desktop machines which areusually with rather fast GPU’s. The present implementation of SlicerAstro providesthe following features that are of great interest for the exploration of radio astronomicalspectral line (in particular H I) data:(a) visualization of astronomical data cubes using the FITS data format;(b) active use of proper astronomical world coordinates and physical units;(c) interactive (semi)automatic smoothing in all three dimensions;(d) interactive 3-D selection of H I sources;(e) interactive H I data modelling coupled to visualization.Figure 1 shows the basic layout of SlicerAstro in the 3DSlicer environment. Theleft panel includes widgets for changing the colour transfer function for 2-D displays andfor changing the properties of the volume rendering, In addition it includes interfacewidgets to control the display proprieties (e.g., user interaction to rotate the 3-D view)and a window for displaying the properties of a data probe coupled to the cursor in thelinked 2-D views. The rest of the display in Figure 1 can be used to display the data in anumber of user specified ways, ranging from a single 3D view to a combination of linkedviews as presented here. In Figure 1 the arrangement of views consists of two linked 3Dviews of two different versions of the same data set (the original data to the left and thefiltered data to the right) at the top and three linked 2D views at the bottom displayingthe original data in three slices: right ascension versus declination, velocity versus rightascension and velocity versus declination. At the top of these three 2D displays there isa slide bar which allows moving through the third coordinate, i.e. velocity, declinationand right ascension respectively.https://www.cambridge.org/core/terms. https://doi.org/10.1017/S174392131700134XDownloaded from https://www.cambridge.org/core. University of Groningen, on 18 Jan 2018 at 08:30:54, subject to the Cambridge Core terms of use, available at3-D visualisation of H I data 307Figure 1. Screenshot of a full view of SlicerAstro in 3DSlicer. The left panel contains widgetswith various control functions described in the text, the right panel is a 5 frame layout showinglinked 3D views of two data sets at the top and three linked 2D views of the left data set atthe bottom. Additional labeling indicates the location of the extension manager control button(for adding packages) and the probe region (lower left) where the astronomical coordinates aredisplayed of the cursor position in the bottom 2D displays.2.1. FilteringIn order to find and enhance faint and extended features in H I data one usually lowersthe resolution in right ascension, declination and/or velocity to increase the signal-to-noise. The optimal shape of the smoothing kernel is of course the one that follows the3D structure of the faint features in the data such that it is just resolved in each of thethree dimensions. The exact shape is, however, not known a priory, so that in practiceone often uses a set of different kernels and then inspects the results to make a finaldecision about which one suits best.Punzo et al. (2016) have investigated this problem and showed that there is an optimumfiltering algorithm that works very effectively on H I data. This filter is an adaptive filter,based on the intensity-gradient driven filter of Perona & Malik (1990), which appearedto be quite effective on H I data with well chosen default parameters. This filter, alsoknown as anisotropic diffusion, does preserve the original resolution for regions wherethe signal-to-noise is high, but adapts to lower resolutions in regions where the signal-to-noise becomes very low but where there still is coherence in the data on larger spatialand/or velocity scales. This method has been implemented in SlicerAstro, in additionto the general Gaussian smoothing. The left 3D panel in Figure 1 shows a version ofthe high resolution data displayed at the left after application of this intensity-gradientdriven filter.2.2. 3-D data selectionHaving an optimized 3-D selection technique is still a problem that has not been solvedsatisfactorily, not only in astronomy, but also not in medical and other visualizationapplications. Moreover, the optimal selection technique highly depends on the naturehttps://www.cambridge.org/core/terms. https://doi.org/10.1017/S174392131700134XDownloaded from https://www.cambridge.org/core. University of Groningen, on 18 Jan 2018 at 08:30:54, subject to the Cambridge Core terms of use, available at308 J. M. van der Hulst, D. Punzo & J. B. T. M. RoerdinkFigure 2. Screenshot of the CloudLasso selection in SlicerAstro. The selection as drawn bythe user in the top right panel with the filtered data is indicated by the yellow line. The selectionis simultaneously displayed in the top left panel displaying the original, unfiltered data. For thefinal selection see Figure 3Figure 3. Screenshot showing three selections, highlighted in blue, ochre and red respectively,with the CloudLasso selection tool. The blue segmentation shows the selection in the CloudLassoregion indicated in Figure 2. The ochre and red segmentations are separate selections of thesecond galaxy and the H I structure between the galaxies respectively. Both the two top 3-Ddisplays and the bottom three 2-D displays show all three selections simultaneously. They canbe switched on or off separately.https://www.cambridge.org/core/terms. https://doi.org/10.1017/S174392131700134XDownloaded from https://www.cambridge.org/core. University of Groningen, on 18 Jan 2018 at 08:30:54, subject to the Cambridge Core terms of use, available at3-D visualisation of H I data 309of the data, i.e. the complexity of the 3-D data structure. Requirements for a 3-D se-lection tool are interactivity and minimal number of user-operations for achieving theselection (i.e. user-friendliness). Yu et al. (2012a) and Yu et al. (2012b) provide a reviewof the state-of-the-art 3-D selection algorithms. In SlicerAstro we implemented theCloudLasso technique Yu et al. (2012) for 3-D selection. The CloudLasso selection usesthe Marching Cubes (MC) algorithm (Wyvill et al. 1986, Lorensen & Cline 1987) for theidentification of regions of voxels with signal inside a user-drawn lasso, i. e. CloudLassois a lasso-constrained Marching Cubes method. The MC method allows spatial selectionof individual structures with a specified signal-to-noise ratio (usually  3) within a lassoregion, even if these lie visually one behind one another, without including the noisyregions in between. A detailed description of the CloudLasso selection can be found inPunzo et al. (2017, in preparation).Figure 2 shows a region selected with the CloudLasso. The selection in Figure 2 hasbeen done on the filtered version of the data (top right panel) and is indicated by theclosed yellow line. In this example the selection is automatically applied to the originaldata shown in the top left panel. The actual selection using the MC algorithm is shownin Figure 3 as the region highlighted in blue. The selection (defined as a segmentationstructure in 3DSlicer) is also shown in the three lower slices in Figure 3, highlightedin the same blue colour. In addition Figure 3 shows two other selections made withthe CloudLasso: a red selection around the faint H I bridge connecting the two galaxiesin this example and a ochre selection highlighting the second galaxy. Also these twoselections are shown in the three slices at the bottom. The CloudLasso implementationallows interactive modification of these selections.The selections are saved in 3DSlicer as segmentation structures, but can be exportedas FITS files in the form of masks. All voxels in such a FITS file are zeros, except forthe ones within a segmentation structure. These are numbered 1, 2, 3, etc. following thenumbering of the segmentations. Conversely a FITS file with masks, produced by anotherprogramme, e.g. SoFiA (Serra et al. 2015), can be read into SlicerAstro, be displayedand modified using the CloudLasso mechanism.2.3. Model aided selectionRather than selecting regions by hand, one can use a model representing the data tohighlight the parts of the 3-D structure that fit the model well and separate those fromthe regions in the 3-D data volume that do not fit the data. A simple example is thefollowing (see Figure 5 in Punzo et al. 2015): one can use a classical tilted ring modelto describe the regularly rotating H I disk of an observed galaxy and use this model tohighlight all parts of the data that fit the model well. All data that does not fit the model,i.e. deviates from regular rotation, can then easily be viewed as it will not be highlighted,i.e. have a contrasting colour. The full 3-D rendering capabilities of SlicerAstro thenallow the user to easily inspect the full 3-D structure of the H I that exhibits anomalousvelocities.This capability is being implemented in SlicerAstro as well, using the automated3-D fitting routine 3D Barolo (Di Teodoro & Fraternali 2015) for fitting the regularlyrotating H I disks in galaxies. This implementation provides both a (partly interactive)modelling capability and allows separating the H I with regular kinematics from the H Iwith anomalous kinematics in the data cubes of observed galaxies. The data used for themodel fitting can be preselected using the CloudLasso procedure described above.In this overview we have presented a new extension, SlicerAstro, that has beenadded to the existing 3DSlicer platform, developed originally for scientific visualisa-tion of medical data. SlicerAstro is not only capable of reading FITS format data,https://www.cambridge.org/core/terms. https://doi.org/10.1017/S174392131700134XDownloaded from https://www.cambridge.org/core. University of Groningen, on 18 Jan 2018 at 08:30:54, subject to the Cambridge Core terms of use, available at310 J. M. van der Hulst, D. Punzo & J. B. T. M. Roerdinkbut also understand and handles astronomical world coordinates and physical units.The first few capabilities implemented in SlicerAstro and described here are filtering,3-D selection and model aided exploration of H I data. The system is still under de-velopment and we expect to add more features in the future. User feedback is greatlyappreciated and can be directed to the first two authors of this paper or be posted onhttps://github.com/Punzo/SlicerAstro.3. AcknowledgmentsD. Punzo and J.M van der Hulst gratefully acknowledge support from the European Re-search Council under the European Union’s Seventh Framework Programme (FP/2007-2013)/ERC Grant Agreement nr. 291531.ReferencesCalabretta, M. R. & Greisen, E. W. 2002, A&A, 395, 1077Duffy, A. R., Meyer, M. J., Staveley-Smith, L., et al. 2012, MNRAS, 426, 3385Di Teodoro, E. M. & Fraternali, F. 2015, MNRAS 451, 3021Gooch, R. 1996, Astronomical Data Analysis Software and Systems V, 101, 80Greisen, E. W. & Calabretta, M. R. 2002, A&A, 395, 1061Greisen, E. W., Calabretta, M. R., Valdes, F. G., & Allen, S. L. 2006, A&A, 446, 747Hassan, A. & Fluke, C. J. 2011, Publications of the Astronomical Society of Australia, 28, 150Johnston, S., Taylor, R., Bailes, M., et al. 2008, Experimental Astronomy, 22, 151Koribalski, B. S. 2012, Publications of the Astronomical Society of Australia, 29, 359Lorensen, W. E. & Cline, H. E. 1987, SIGGRAPH Comput. Graph. vol. 21, nr. 4, 163Norris, R. P. 1994, Astronomical Data Analysis Software and Systems III, 61, 51Oosterloo, T., Verheijen, M., & van Cappellen, W. 2010, ISKAF2010 Science Meeting, 43Perona, P & Malik, J. 1990, IEEE, 12, 629Punzo, D., van der Hulst, J. M., & Roerdink, J. B. T. M. 2016, Astronomy and Computing, 17,163Punzo, D., van der Hulst, J. M., Roerdink, J. B. T. M., et al. 2015, Astronomy and Computing,12, 86Sancisi, R., Fraternali, F., Oosterloo, T., & van der Hulst, T. 2008, A&A Review, 15, 189Serra, P., Westmeier, T., Giese, N., et al. 2015, MNRAS, 448, 1922Verheijen, M., Oosterloo, T., Heald, G., & van Cappellen, W. 2009, Panoramic Radio Astronomy:Wide-field 1-2 GHz Research on Galaxy Evolution, 10Wells, D. C., Greisen, E. W., & Harten, R. H. 1981, A&A Suppl., 44, 363Whiting, M. T. 2012, MNRAS, 421, 3242Wyvill, G., McPheeters, C., & Wyvill, B. 1986, The Visual Computer, vol. 2, nr. 4, 227Yu, L., Efstatiou, K., Isenberg, P., & Isenberg, T. 2012, IEEE Transactions on Visualizationand Computer Graphics, vol. 18, nr. 12, 2245Yu, L., Efstatiou, K., Isenberg, P., & Isenberg, T. 2012, IEEE Transactions on Visualizationand Computer Graphics, vol. 22, nr. 1, 1077https://www.cambridge.org/core/terms. https://doi.org/10.1017/S174392131700134XDownloaded from https://www.cambridge.org/core. University of Groningen, on 18 Jan 2018 at 08:30:54, subject to the Cambridge Core terms of use, available at

ARTS repository - University of Groningen

   University of Groningen3-D interactive visualisation tools for Hi spectral line imagingvan der Hulst, J. M.; Punzo, D.; Roerdink, J. B. T. M.Published in:Proceedings of the International Astronomical Union, IAU SymposiumDOI:10.1017/S174392131700134XIMPORTANT NOTE: You are advised to consult the publisher's version (publisher's PDF) if you wish to cite fromit. Please check the document version below.Document VersionPublisher's PDF, also known as Version of recordPublication date:2016Link to publication in University of Groningen/UMCG research databaseCitation for published version (APA):van der Hulst, J. M., Punzo, D., & Roerdink, J. B. T. M. (2016). 3-D interactive visualisation tools for Hispectral line imaging. Proceedings of the International Astronomical Union, IAU Symposium, 325, 305-310.https://doi.org/10.1017/S174392131700134XCopyrightOther than for strictly personal use, it is not permitted to download or to forward/distribute the text or part of it without the consent of theauthor(s) and/or copyright holder(s), unless the work is under an open content license (like Creative Commons).The publication may also be distributed here under the terms of Article 25fa of the Dutch Copyright Act, indicated by the “Taverne” license.More information can be found on the University of Groningen website: https://www.rug.nl/library/open-access/self-archiving-pure/taverne-amendment.Take-down policyIf you believe that this document breaches copyright please contact us providing details, and we will remove access to the work immediatelyand investigate your claim.Downloaded from the University of Groningen/UMCG research database (Pure): http://www.rug.nl/research/portal. For technical reasons thenumber of authors shown on this cover page is limited to 10 maximum.Download date: 21-06-2022AstroinformaticsProceedings IAU Symposium No. 325, 2016M. Brescia, S.G. Djorgovski, E. Feigelson,G. Longo & S. Cavuoti, eds.c© International Astronomical Union 2017doi:10.1017/S174392131700134X3-D interactive visualisation tools for H Ispectral line imagingJ. M. van der Hulst, D. Punzo1 and J. B. T. M. Roerdink21Kapteyn Astronomical Institute, University of Groningen,Landleven 12, NL-9747AD, Groningen, the Netherlandsemail: j.m.van.der.hulst@rug.nlpunzo@astro.rug.nl2 Johann Bernoulli Institute for Mathematics and Computer Science, University of Groningen,Nijenborgh 9, NL-9747AG Groningen, the Netherlandsemail: j.b.t.m.roerdink@rug.nlAbstract. Upcoming H I surveys will deliver such large datasets that automated processingusing the full 3-D information to find and characterize H I objects is unavoidable. Full 3-Dvisualization is an essential tool for enabling qualitative and quantitative inspection and analysisof the 3-D data, which is often complex in nature. Here we present SlicerAstro, an open-sourceextension of 3DSlicer, a multi-platform open source software package for visualization andmedical image processing, which we developed for the inspection and analysis of H I spectral linedata. We describe its initial capabilities, including 3-D filtering, 3-D selection and comparativemodelling.Keywords. techniques: image processing; methods: data analysis; galaxies: structure, evolution,interactions, ISM1. IntroductionUpcoming H I surveys with instruments such as ASKAP (Australian Square KilometreArray Pathfinder, Johnston et al. 2008) and Apertif on the WSRT (Westerbork SynthesisRadio Telescope, Verheijen et al. 2009, Oosterloo et al. 2010) will provide large dataflows, in the regime of a few TB per day. Automation of the calibrating and imaging isimperative, and so is the automation of finding objects in the noisy H I data cubes. Theexpectation is that an H I survey with each of these instruments is capable of deliveringof the order of 100 objects per day (Duffy et al. 2012). Automated source finders suchas Duchamp (Whiting 2012) or SoFiA (Serra et al. 2015) will locate the detected objects.Yet in many cases, in particular when the objects are well resolved, further inspection isrequired to fully explore the characteristics of these object and interpret the subtle signsin the H I data of the various mechanisms of gas accretion and removal in galaxies (foran extended review of these see Sancisi et al. 2008).In the early 90’s it was realised that viewers for radio-astronomical spectral line datawould benefit greatly from advanced 3-D visualization software (Norris 1994). It was veryclear already then that full a 3-D approach would greatly help understanding of the 3-Dstructure of the radio data. The type of 2-D data slicing commonly used (i.e., channelmovies), and implemented in packages such as karma (Gooch 1996), is useful but limitedin that it misses the capability to get a full 3-D view of the data. Recently, both Hassan& Fluke (2011) and Koribalski (2012) pointed this out and and described need for 3-Dvisualisation capabilities in the context of upcoming H I surveys.305https://www.cambridge.org/core/terms. https://doi.org/10.1017/S174392131700134XDownloaded from https://www.cambridge.org/core. University of Groningen, on 18 Jan 2018 at 08:30:54, subject to the Cambridge Core terms of use, available at306 J. M. van der Hulst, D. Punzo & J. B. T. M. RoerdinkPunzo et al. (2015) discussed the requirements for a fully interactive visualization tool:coupled 1-D/2-D/3-D visualization, quantitative and comparative capabilities, combinedwith supervised semi-automated analysis. In addition the source code must be open, mod-ular, well documented, and well maintained. Punzo et al. (2015) reviewed several existingpackages and decided to develop a module for 3DSlicer (URL: https://www.slicer.org/)which adds the following characteristics to the already large number of capabilities ofthe current 3DSlicer system: (i) the ability to read FITS (Flexible Image TransportSystem, Wells et al. 1981) files and handle the astronomical world coordinates system(WCS, Calabretta & Greisen 2002, Greisen & Calabretta 2002, and Greisen et al. 2006),(ii) coupled 1-D, 2-D and 3-D display of the data and fast rendering, (iii) semi-automatedfiltering methods to recover faint emission using the information in all three dimensions(Punzo et al. 2016), (iv) 3-D selection and (v) use of 3-D modelling to characterise andselect the regularly rotating H I disks in galaxies. This paper gives a brief overview of thecapabilities of SlicerAstro. For a detailed description see Punzo et al. (2015), Punzoet al. (2016) and Punzo et al. (2017, in preparation).2. SlicerAstroSlicerAstro (URL: https://github.com/Punzo/SlicerAstro) is an open-source exten-sion of 3DSlicer, provided via its built-in extension manager. All functionality withinSlicerAstro is geared toward handling 3-D radio astronomical data, but makes use of allbuilt in features of the core software of 3DSlicer, fully exploiting the fast 3-D rendering,linked display capabilities and segmentation or region of interest selection. It will be ableto handle 3-D data cubes of op to 109 voxels on modern desktop machines which areusually with rather fast GPU’s. The present implementation of SlicerAstro providesthe following features that are of great interest for the exploration of radio astronomicalspectral line (in particular H I) data:(a) visualization of astronomical data cubes using the FITS data format;(b) active use of proper astronomical world coordinates and physical units;(c) interactive (semi)automatic smoothing in all three dimensions;(d) interactive 3-D selection of H I sources;(e) interactive H I data modelling coupled to visualization.Figure 1 shows the basic layout of SlicerAstro in the 3DSlicer environment. Theleft panel includes widgets for changing the colour transfer function for 2-D displays andfor changing the properties of the volume rendering, In addition it includes interfacewidgets to control the display proprieties (e.g., user interaction to rotate the 3-D view)and a window for displaying the properties of a data probe coupled to the cursor in thelinked 2-D views. The rest of the display in Figure 1 can be used to display the data in anumber of user specified ways, ranging from a single 3D view to a combination of linkedviews as presented here. In Figure 1 the arrangement of views consists of two linked 3Dviews of two different versions of the same data set (the original data to the left and thefiltered data to the right) at the top and three linked 2D views at the bottom displayingthe original data in three slices: right ascension versus declination, velocity versus rightascension and velocity versus declination. At the top of these three 2D displays there isa slide bar which allows moving through the third coordinate, i.e. velocity, declinationand right ascension respectively.https://www.cambridge.org/core/terms. https://doi.org/10.1017/S174392131700134XDownloaded from https://www.cambridge.org/core. University of Groningen, on 18 Jan 2018 at 08:30:54, subject to the Cambridge Core terms of use, available at3-D visualisation of H I data 307Figure 1. Screenshot of a full view of SlicerAstro in 3DSlicer. The left panel contains widgetswith various control functions described in the text, the right panel is a 5 frame layout showinglinked 3D views of two data sets at the top and three linked 2D views of the left data set atthe bottom. Additional labeling indicates the location of the extension manager control button(for adding packages) and the probe region (lower left) where the astronomical coordinates aredisplayed of the cursor position in the bottom 2D displays.2.1. FilteringIn order to find and enhance faint and extended features in H I data one usually lowersthe resolution in right ascension, declination and/or velocity to increase the signal-to-noise. The optimal shape of the smoothing kernel is of course the one that follows the3D structure of the faint features in the data such that it is just resolved in each of thethree dimensions. The exact shape is, however, not known a priory, so that in practiceone often uses a set of different kernels and then inspects the results to make a finaldecision about which one suits best.Punzo et al. (2016) have investigated this problem and showed that there is an optimumfiltering algorithm that works very effectively on H I data. This filter is an adaptive filter,based on the intensity-gradient driven filter of Perona & Malik (1990), which appearedto be quite effective on H I data with well chosen default parameters. This filter, alsoknown as anisotropic diffusion, does preserve the original resolution for regions wherethe signal-to-noise is high, but adapts to lower resolutions in regions where the signal-to-noise becomes very low but where there still is coherence in the data on larger spatialand/or velocity scales. This method has been implemented in SlicerAstro, in additionto the general Gaussian smoothing. The left 3D panel in Figure 1 shows a version ofthe high resolution data displayed at the left after application of this intensity-gradientdriven filter.2.2. 3-D data selectionHaving an optimized 3-D selection technique is still a problem that has not been solvedsatisfactorily, not only in astronomy, but also not in medical and other visualizationapplications. Moreover, the optimal selection technique highly depends on the naturehttps://www.cambridge.org/core/terms. https://doi.org/10.1017/S174392131700134XDownloaded from https://www.cambridge.org/core. University of Groningen, on 18 Jan 2018 at 08:30:54, subject to the Cambridge Core terms of use, available at308 J. M. van der Hulst, D. Punzo & J. B. T. M. RoerdinkFigure 2. Screenshot of the CloudLasso selection in SlicerAstro. The selection as drawn bythe user in the top right panel with the filtered data is indicated by the yellow line. The selectionis simultaneously displayed in the top left panel displaying the original, unfiltered data. For thefinal selection see Figure 3Figure 3. Screenshot showing three selections, highlighted in blue, ochre and red respectively,with the CloudLasso selection tool. The blue segmentation shows the selection in the CloudLassoregion indicated in Figure 2. The ochre and red segmentations are separate selections of thesecond galaxy and the H I structure between the galaxies respectively. Both the two top 3-Ddisplays and the bottom three 2-D displays show all three selections simultaneously. They canbe switched on or off separately.https://www.cambridge.org/core/terms. https://doi.org/10.1017/S174392131700134XDownloaded from https://www.cambridge.org/core. University of Groningen, on 18 Jan 2018 at 08:30:54, subject to the Cambridge Core terms of use, available at3-D visualisation of H I data 309of the data, i.e. the complexity of the 3-D data structure. Requirements for a 3-D se-lection tool are interactivity and minimal number of user-operations for achieving theselection (i.e. user-friendliness). Yu et al. (2012a) and Yu et al. (2012b) provide a reviewof the state-of-the-art 3-D selection algorithms. In SlicerAstro we implemented theCloudLasso technique Yu et al. (2012) for 3-D selection. The CloudLasso selection usesthe Marching Cubes (MC) algorithm (Wyvill et al. 1986, Lorensen & Cline 1987) for theidentification of regions of voxels with signal inside a user-drawn lasso, i. e. CloudLassois a lasso-constrained Marching Cubes method. The MC method allows spatial selectionof individual structures with a specified signal-to-noise ratio (usually  3) within a lassoregion, even if these lie visually one behind one another, without including the noisyregions in between. A detailed description of the CloudLasso selection can be found inPunzo et al. (2017, in preparation).Figure 2 shows a region selected with the CloudLasso. The selection in Figure 2 hasbeen done on the filtered version of the data (top right panel) and is indicated by theclosed yellow line. In this example the selection is automatically applied to the originaldata shown in the top left panel. The actual selection using the MC algorithm is shownin Figure 3 as the region highlighted in blue. The selection (defined as a segmentationstructure in 3DSlicer) is also shown in the three lower slices in Figure 3, highlightedin the same blue colour. In addition Figure 3 shows two other selections made withthe CloudLasso: a red selection around the faint H I bridge connecting the two galaxiesin this example and a ochre selection highlighting the second galaxy. Also these twoselections are shown in the three slices at the bottom. The CloudLasso implementationallows interactive modification of these selections.The selections are saved in 3DSlicer as segmentation structures, but can be exportedas FITS files in the form of masks. All voxels in such a FITS file are zeros, except forthe ones within a segmentation structure. These are numbered 1, 2, 3, etc. following thenumbering of the segmentations. Conversely a FITS file with masks, produced by anotherprogramme, e.g. SoFiA (Serra et al. 2015), can be read into SlicerAstro, be displayedand modified using the CloudLasso mechanism.2.3. Model aided selectionRather than selecting regions by hand, one can use a model representing the data tohighlight the parts of the 3-D structure that fit the model well and separate those fromthe regions in the 3-D data volume that do not fit the data. A simple example is thefollowing (see Figure 5 in Punzo et al. 2015): one can use a classical tilted ring modelto describe the regularly rotating H I disk of an observed galaxy and use this model tohighlight all parts of the data that fit the model well. All data that does not fit the model,i.e. deviates from regular rotation, can then easily be viewed as it will not be highlighted,i.e. have a contrasting colour. The full 3-D rendering capabilities of SlicerAstro thenallow the user to easily inspect the full 3-D structure of the H I that exhibits anomalousvelocities.This capability is being implemented in SlicerAstro as well, using the automated3-D fitting routine 3D Barolo (Di Teodoro & Fraternali 2015) for fitting the regularlyrotating H I disks in galaxies. This implementation provides both a (partly interactive)modelling capability and allows separating the H I with regular kinematics from the H Iwith anomalous kinematics in the data cubes of observed galaxies. The data used for themodel fitting can be preselected using the CloudLasso procedure described above.In this overview we have presented a new extension, SlicerAstro, that has beenadded to the existing 3DSlicer platform, developed originally for scientific visualisa-tion of medical data. SlicerAstro is not only capable of reading FITS format data,https://www.cambridge.org/core/terms. https://doi.org/10.1017/S174392131700134XDownloaded from https://www.cambridge.org/core. University of Groningen, on 18 Jan 2018 at 08:30:54, subject to the Cambridge Core terms of use, available at310 J. M. van der Hulst, D. Punzo & J. B. T. M. Roerdinkbut also understand and handles astronomical world coordinates and physical units.The first few capabilities implemented in SlicerAstro and described here are filtering,3-D selection and model aided exploration of H I data. The system is still under de-velopment and we expect to add more features in the future. User feedback is greatlyappreciated and can be directed to the first two authors of this paper or be posted onhttps://github.com/Punzo/SlicerAstro.3. AcknowledgmentsD. Punzo and J.M van der Hulst gratefully acknowledge support from the European Re-search Council under the European Union’s Seventh Framework Programme (FP/2007-2013)/ERC Grant Agreement nr. 291531.ReferencesCalabretta, M. R. & Greisen, E. W. 2002, A&A, 395, 1077Duffy, A. R., Meyer, M. J., Staveley-Smith, L., et al. 2012, MNRAS, 426, 3385Di Teodoro, E. M. & Fraternali, F. 2015, MNRAS 451, 3021Gooch, R. 1996, Astronomical Data Analysis Software and Systems V, 101, 80Greisen, E. W. & Calabretta, M. R. 2002, A&A, 395, 1061Greisen, E. W., Calabretta, M. R., Valdes, F. G., & Allen, S. L. 2006, A&A, 446, 747Hassan, A. & Fluke, C. J. 2011, Publications of the Astronomical Society of Australia, 28, 150Johnston, S., Taylor, R., Bailes, M., et al. 2008, Experimental Astronomy, 22, 151Koribalski, B. S. 2012, Publications of the Astronomical Society of Australia, 29, 359Lorensen, W. E. & Cline, H. E. 1987, SIGGRAPH Comput. Graph. vol. 21, nr. 4, 163Norris, R. P. 1994, Astronomical Data Analysis Software and Systems III, 61, 51Oosterloo, T., Verheijen, M., & van Cappellen, W. 2010, ISKAF2010 Science Meeting, 43Perona, P & Malik, J. 1990, IEEE, 12, 629Punzo, D., van der Hulst, J. M., & Roerdink, J. B. T. M. 2016, Astronomy and Computing, 17,163Punzo, D., van der Hulst, J. M., Roerdink, J. B. T. M., et al. 2015, Astronomy and Computing,12, 86Sancisi, R., Fraternali, F., Oosterloo, T., & van der Hulst, T. 2008, A&A Review, 15, 189Serra, P., Westmeier, T., Giese, N., et al. 2015, MNRAS, 448, 1922Verheijen, M., Oosterloo, T., Heald, G., & van Cappellen, W. 2009, Panoramic Radio Astronomy:Wide-field 1-2 GHz Research on Galaxy Evolution, 10Wells, D. C., Greisen, E. W., & Harten, R. H. 1981, A&A Suppl., 44, 363Whiting, M. T. 2012, MNRAS, 421, 3242Wyvill, G., McPheeters, C., & Wyvill, B. 1986, The Visual Computer, vol. 2, nr. 4, 227Yu, L., Efstatiou, K., Isenberg, P., & Isenberg, T. 2012, IEEE Transactions on Visualizationand Computer Graphics, vol. 18, nr. 12, 2245Yu, L., Efstatiou, K., Isenberg, P., & Isenberg, T. 2012, IEEE Transactions on Visualizationand Computer Graphics, vol. 22, nr. 1, 1077https://www.cambridge.org/core/terms. https://doi.org/10.1017/S174392131700134XDownloaded from https://www.cambridge.org/core. University of Groningen, on 18 Jan 2018 at 08:30:54, subject to the Cambridge Core terms of use, available at

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   University of Groningen3-D interactive visualisation tools for Hi spectral line imagingvan der Hulst, J. M.; Punzo, D.; Roerdink, J. B. T. M.Published in:Proceedings of the International Astronomical Union, IAU SymposiumDOI:10.1017/S174392131700134XIMPORTANT NOTE: You are advised to consult the publisher's version (publisher's PDF) if you wish to cite fromit. Please check the document version below.Document VersionPublisher's PDF, also known as Version of recordPublication date:2016Link to publication in University of Groningen/UMCG research databaseCitation for published version (APA):van der Hulst, J. M., Punzo, D., & Roerdink, J. B. T. M. (2016). 3-D interactive visualisation tools for Hispectral line imaging. Proceedings of the International Astronomical Union, IAU Symposium, 325, 305-310.https://doi.org/10.1017/S174392131700134XCopyrightOther than for strictly personal use, it is not permitted to download or to forward/distribute the text or part of it without the consent of theauthor(s) and/or copyright holder(s), unless the work is under an open content license (like Creative Commons).The publication may also be distributed here under the terms of Article 25fa of the Dutch Copyright Act, indicated by the “Taverne” license.More information can be found on the University of Groningen website: https://www.rug.nl/library/open-access/self-archiving-pure/taverne-amendment.Take-down policyIf you believe that this document breaches copyright please contact us providing details, and we will remove access to the work immediatelyand investigate your claim.Downloaded from the University of Groningen/UMCG research database (Pure): http://www.rug.nl/research/portal. For technical reasons thenumber of authors shown on this cover page is limited to 10 maximum.Download date: 28-04-2023AstroinformaticsProceedings IAU Symposium No. 325, 2016M. Brescia, S.G. Djorgovski, E. Feigelson,G. Longo & S. Cavuoti, eds.c© International Astronomical Union 2017doi:10.1017/S174392131700134X3-D interactive visualisation tools for H Ispectral line imagingJ. M. van der Hulst, D. Punzo1 and J. B. T. M. Roerdink21Kapteyn Astronomical Institute, University of Groningen,Landleven 12, NL-9747AD, Groningen, the Netherlandsemail: j.m.van.der.hulst@rug.nlpunzo@astro.rug.nl2 Johann Bernoulli Institute for Mathematics and Computer Science, University of Groningen,Nijenborgh 9, NL-9747AG Groningen, the Netherlandsemail: j.b.t.m.roerdink@rug.nlAbstract. Upcoming H I surveys will deliver such large datasets that automated processingusing the full 3-D information to find and characterize H I objects is unavoidable. Full 3-Dvisualization is an essential tool for enabling qualitative and quantitative inspection and analysisof the 3-D data, which is often complex in nature. Here we present SlicerAstro, an open-sourceextension of 3DSlicer, a multi-platform open source software package for visualization andmedical image processing, which we developed for the inspection and analysis of H I spectral linedata. We describe its initial capabilities, including 3-D filtering, 3-D selection and comparativemodelling.Keywords. techniques: image processing; methods: data analysis; galaxies: structure, evolution,interactions, ISM1. IntroductionUpcoming H I surveys with instruments such as ASKAP (Australian Square KilometreArray Pathfinder, Johnston et al. 2008) and Apertif on the WSRT (Westerbork SynthesisRadio Telescope, Verheijen et al. 2009, Oosterloo et al. 2010) will provide large dataflows, in the regime of a few TB per day. Automation of the calibrating and imaging isimperative, and so is the automation of finding objects in the noisy H I data cubes. Theexpectation is that an H I survey with each of these instruments is capable of deliveringof the order of 100 objects per day (Duffy et al. 2012). Automated source finders suchas Duchamp (Whiting 2012) or SoFiA (Serra et al. 2015) will locate the detected objects.Yet in many cases, in particular when the objects are well resolved, further inspection isrequired to fully explore the characteristics of these object and interpret the subtle signsin the H I data of the various mechanisms of gas accretion and removal in galaxies (foran extended review of these see Sancisi et al. 2008).In the early 90’s it was realised that viewers for radio-astronomical spectral line datawould benefit greatly from advanced 3-D visualization software (Norris 1994). It was veryclear already then that full a 3-D approach would greatly help understanding of the 3-Dstructure of the radio data. The type of 2-D data slicing commonly used (i.e., channelmovies), and implemented in packages such as karma (Gooch 1996), is useful but limitedin that it misses the capability to get a full 3-D view of the data. Recently, both Hassan& Fluke (2011) and Koribalski (2012) pointed this out and and described need for 3-Dvisualisation capabilities in the context of upcoming H I surveys.305https://www.cambridge.org/core/terms. https://doi.org/10.1017/S174392131700134XDownloaded from https://www.cambridge.org/core. University of Groningen, on 18 Jan 2018 at 08:30:54, subject to the Cambridge Core terms of use, available at306 J. M. van der Hulst, D. Punzo & J. B. T. M. RoerdinkPunzo et al. (2015) discussed the requirements for a fully interactive visualization tool:coupled 1-D/2-D/3-D visualization, quantitative and comparative capabilities, combinedwith supervised semi-automated analysis. In addition the source code must be open, mod-ular, well documented, and well maintained. Punzo et al. (2015) reviewed several existingpackages and decided to develop a module for 3DSlicer (URL: https://www.slicer.org/)which adds the following characteristics to the already large number of capabilities ofthe current 3DSlicer system: (i) the ability to read FITS (Flexible Image TransportSystem, Wells et al. 1981) files and handle the astronomical world coordinates system(WCS, Calabretta & Greisen 2002, Greisen & Calabretta 2002, and Greisen et al. 2006),(ii) coupled 1-D, 2-D and 3-D display of the data and fast rendering, (iii) semi-automatedfiltering methods to recover faint emission using the information in all three dimensions(Punzo et al. 2016), (iv) 3-D selection and (v) use of 3-D modelling to characterise andselect the regularly rotating H I disks in galaxies. This paper gives a brief overview of thecapabilities of SlicerAstro. For a detailed description see Punzo et al. (2015), Punzoet al. (2016) and Punzo et al. (2017, in preparation).2. SlicerAstroSlicerAstro (URL: https://github.com/Punzo/SlicerAstro) is an open-source exten-sion of 3DSlicer, provided via its built-in extension manager. All functionality withinSlicerAstro is geared toward handling 3-D radio astronomical data, but makes use of allbuilt in features of the core software of 3DSlicer, fully exploiting the fast 3-D rendering,linked display capabilities and segmentation or region of interest selection. It will be ableto handle 3-D data cubes of op to 109 voxels on modern desktop machines which areusually with rather fast GPU’s. The present implementation of SlicerAstro providesthe following features that are of great interest for the exploration of radio astronomicalspectral line (in particular H I) data:(a) visualization of astronomical data cubes using the FITS data format;(b) active use of proper astronomical world coordinates and physical units;(c) interactive (semi)automatic smoothing in all three dimensions;(d) interactive 3-D selection of H I sources;(e) interactive H I data modelling coupled to visualization.Figure 1 shows the basic layout of SlicerAstro in the 3DSlicer environment. Theleft panel includes widgets for changing the colour transfer function for 2-D displays andfor changing the properties of the volume rendering, In addition it includes interfacewidgets to control the display proprieties (e.g., user interaction to rotate the 3-D view)and a window for displaying the properties of a data probe coupled to the cursor in thelinked 2-D views. The rest of the display in Figure 1 can be used to display the data in anumber of user specified ways, ranging from a single 3D view to a combination of linkedviews as presented here. In Figure 1 the arrangement of views consists of two linked 3Dviews of two different versions of the same data set (the original data to the left and thefiltered data to the right) at the top and three linked 2D views at the bottom displayingthe original data in three slices: right ascension versus declination, velocity versus rightascension and velocity versus declination. At the top of these three 2D displays there isa slide bar which allows moving through the third coordinate, i.e. velocity, declinationand right ascension respectively.https://www.cambridge.org/core/terms. https://doi.org/10.1017/S174392131700134XDownloaded from https://www.cambridge.org/core. University of Groningen, on 18 Jan 2018 at 08:30:54, subject to the Cambridge Core terms of use, available at3-D visualisation of H I data 307Figure 1. Screenshot of a full view of SlicerAstro in 3DSlicer. The left panel contains widgetswith various control functions described in the text, the right panel is a 5 frame layout showinglinked 3D views of two data sets at the top and three linked 2D views of the left data set atthe bottom. Additional labeling indicates the location of the extension manager control button(for adding packages) and the probe region (lower left) where the astronomical coordinates aredisplayed of the cursor position in the bottom 2D displays.2.1. FilteringIn order to find and enhance faint and extended features in H I data one usually lowersthe resolution in right ascension, declination and/or velocity to increase the signal-to-noise. The optimal shape of the smoothing kernel is of course the one that follows the3D structure of the faint features in the data such that it is just resolved in each of thethree dimensions. The exact shape is, however, not known a priory, so that in practiceone often uses a set of different kernels and then inspects the results to make a finaldecision about which one suits best.Punzo et al. (2016) have investigated this problem and showed that there is an optimumfiltering algorithm that works very effectively on H I data. This filter is an adaptive filter,based on the intensity-gradient driven filter of Perona & Malik (1990), which appearedto be quite effective on H I data with well chosen default parameters. This filter, alsoknown as anisotropic diffusion, does preserve the original resolution for regions wherethe signal-to-noise is high, but adapts to lower resolutions in regions where the signal-to-noise becomes very low but where there still is coherence in the data on larger spatialand/or velocity scales. This method has been implemented in SlicerAstro, in additionto the general Gaussian smoothing. The left 3D panel in Figure 1 shows a version ofthe high resolution data displayed at the left after application of this intensity-gradientdriven filter.2.2. 3-D data selectionHaving an optimized 3-D selection technique is still a problem that has not been solvedsatisfactorily, not only in astronomy, but also not in medical and other visualizationapplications. Moreover, the optimal selection technique highly depends on the naturehttps://www.cambridge.org/core/terms. https://doi.org/10.1017/S174392131700134XDownloaded from https://www.cambridge.org/core. University of Groningen, on 18 Jan 2018 at 08:30:54, subject to the Cambridge Core terms of use, available at308 J. M. van der Hulst, D. Punzo & J. B. T. M. RoerdinkFigure 2. Screenshot of the CloudLasso selection in SlicerAstro. The selection as drawn bythe user in the top right panel with the filtered data is indicated by the yellow line. The selectionis simultaneously displayed in the top left panel displaying the original, unfiltered data. For thefinal selection see Figure 3Figure 3. Screenshot showing three selections, highlighted in blue, ochre and red respectively,with the CloudLasso selection tool. The blue segmentation shows the selection in the CloudLassoregion indicated in Figure 2. The ochre and red segmentations are separate selections of thesecond galaxy and the H I structure between the galaxies respectively. Both the two top 3-Ddisplays and the bottom three 2-D displays show all three selections simultaneously. They canbe switched on or off separately.https://www.cambridge.org/core/terms. https://doi.org/10.1017/S174392131700134XDownloaded from https://www.cambridge.org/core. University of Groningen, on 18 Jan 2018 at 08:30:54, subject to the Cambridge Core terms of use, available at3-D visualisation of H I data 309of the data, i.e. the complexity of the 3-D data structure. Requirements for a 3-D se-lection tool are interactivity and minimal number of user-operations for achieving theselection (i.e. user-friendliness). Yu et al. (2012a) and Yu et al. (2012b) provide a reviewof the state-of-the-art 3-D selection algorithms. In SlicerAstro we implemented theCloudLasso technique Yu et al. (2012) for 3-D selection. The CloudLasso selection usesthe Marching Cubes (MC) algorithm (Wyvill et al. 1986, Lorensen & Cline 1987) for theidentification of regions of voxels with signal inside a user-drawn lasso, i. e. CloudLassois a lasso-constrained Marching Cubes method. The MC method allows spatial selectionof individual structures with a specified signal-to-noise ratio (usually  3) within a lassoregion, even if these lie visually one behind one another, without including the noisyregions in between. A detailed description of the CloudLasso selection can be found inPunzo et al. (2017, in preparation).Figure 2 shows a region selected with the CloudLasso. The selection in Figure 2 hasbeen done on the filtered version of the data (top right panel) and is indicated by theclosed yellow line. In this example the selection is automatically applied to the originaldata shown in the top left panel. The actual selection using the MC algorithm is shownin Figure 3 as the region highlighted in blue. The selection (defined as a segmentationstructure in 3DSlicer) is also shown in the three lower slices in Figure 3, highlightedin the same blue colour. In addition Figure 3 shows two other selections made withthe CloudLasso: a red selection around the faint H I bridge connecting the two galaxiesin this example and a ochre selection highlighting the second galaxy. Also these twoselections are shown in the three slices at the bottom. The CloudLasso implementationallows interactive modification of these selections.The selections are saved in 3DSlicer as segmentation structures, but can be exportedas FITS files in the form of masks. All voxels in such a FITS file are zeros, except forthe ones within a segmentation structure. These are numbered 1, 2, 3, etc. following thenumbering of the segmentations. Conversely a FITS file with masks, produced by anotherprogramme, e.g. SoFiA (Serra et al. 2015), can be read into SlicerAstro, be displayedand modified using the CloudLasso mechanism.2.3. Model aided selectionRather than selecting regions by hand, one can use a model representing the data tohighlight the parts of the 3-D structure that fit the model well and separate those fromthe regions in the 3-D data volume that do not fit the data. A simple example is thefollowing (see Figure 5 in Punzo et al. 2015): one can use a classical tilted ring modelto describe the regularly rotating H I disk of an observed galaxy and use this model tohighlight all parts of the data that fit the model well. All data that does not fit the model,i.e. deviates from regular rotation, can then easily be viewed as it will not be highlighted,i.e. have a contrasting colour. The full 3-D rendering capabilities of SlicerAstro thenallow the user to easily inspect the full 3-D structure of the H I that exhibits anomalousvelocities.This capability is being implemented in SlicerAstro as well, using the automated3-D fitting routine 3D Barolo (Di Teodoro & Fraternali 2015) for fitting the regularlyrotating H I disks in galaxies. This implementation provides both a (partly interactive)modelling capability and allows separating the H I with regular kinematics from the H Iwith anomalous kinematics in the data cubes of observed galaxies. The data used for themodel fitting can be preselected using the CloudLasso procedure described above.In this overview we have presented a new extension, SlicerAstro, that has beenadded to the existing 3DSlicer platform, developed originally for scientific visualisa-tion of medical data. SlicerAstro is not only capable of reading FITS format data,https://www.cambridge.org/core/terms. https://doi.org/10.1017/S174392131700134XDownloaded from https://www.cambridge.org/core. University of Groningen, on 18 Jan 2018 at 08:30:54, subject to the Cambridge Core terms of use, available at310 J. M. van der Hulst, D. Punzo & J. B. T. M. Roerdinkbut also understand and handles astronomical world coordinates and physical units.The first few capabilities implemented in SlicerAstro and described here are filtering,3-D selection and model aided exploration of H I data. The system is still under de-velopment and we expect to add more features in the future. User feedback is greatlyappreciated and can be directed to the first two authors of this paper or be posted onhttps://github.com/Punzo/SlicerAstro.3. AcknowledgmentsD. Punzo and J.M van der Hulst gratefully acknowledge support from the European Re-search Council under the European Union’s Seventh Framework Programme (FP/2007-2013)/ERC Grant Agreement nr. 291531.ReferencesCalabretta, M. R. & Greisen, E. W. 2002, A&A, 395, 1077Duffy, A. R., Meyer, M. J., Staveley-Smith, L., et al. 2012, MNRAS, 426, 3385Di Teodoro, E. M. & Fraternali, F. 2015, MNRAS 451, 3021Gooch, R. 1996, Astronomical Data Analysis Software and Systems V, 101, 80Greisen, E. W. & Calabretta, M. R. 2002, A&A, 395, 1061Greisen, E. W., Calabretta, M. R., Valdes, F. G., & Allen, S. L. 2006, A&A, 446, 747Hassan, A. & Fluke, C. J. 2011, Publications of the Astronomical Society of Australia, 28, 150Johnston, S., Taylor, R., Bailes, M., et al. 2008, Experimental Astronomy, 22, 151Koribalski, B. S. 2012, Publications of the Astronomical Society of Australia, 29, 359Lorensen, W. E. & Cline, H. E. 1987, SIGGRAPH Comput. Graph. vol. 21, nr. 4, 163Norris, R. P. 1994, Astronomical Data Analysis Software and Systems III, 61, 51Oosterloo, T., Verheijen, M., & van Cappellen, W. 2010, ISKAF2010 Science Meeting, 43Perona, P & Malik, J. 1990, IEEE, 12, 629Punzo, D., van der Hulst, J. M., & Roerdink, J. B. T. M. 2016, Astronomy and Computing, 17,163Punzo, D., van der Hulst, J. M., Roerdink, J. B. T. M., et al. 2015, Astronomy and Computing,12, 86Sancisi, R., Fraternali, F., Oosterloo, T., & van der Hulst, T. 2008, A&A Review, 15, 189Serra, P., Westmeier, T., Giese, N., et al. 2015, MNRAS, 448, 1922Verheijen, M., Oosterloo, T., Heald, G., & van Cappellen, W. 2009, Panoramic Radio Astronomy:Wide-field 1-2 GHz Research on Galaxy Evolution, 10Wells, D. C., Greisen, E. W., & Harten, R. H. 1981, A&A Suppl., 44, 363Whiting, M. T. 2012, MNRAS, 421, 3242Wyvill, G., McPheeters, C., & Wyvill, B. 1986, The Visual Computer, vol. 2, nr. 4, 227Yu, L., Efstatiou, K., Isenberg, P., & Isenberg, T. 2012, IEEE Transactions on Visualizationand Computer Graphics, vol. 18, nr. 12, 2245Yu, L., Efstatiou, K., Isenberg, P., & Isenberg, T. 2012, IEEE Transactions on Visualizationand Computer Graphics, vol. 22, nr. 1, 1077https://www.cambridge.org/core/terms. https://doi.org/10.1017/S174392131700134XDownloaded from https://www.cambridge.org/core. University of Groningen, on 18 Jan 2018 at 08:30:54, subject to the Cambridge Core terms of use, available at

Dissertations of the University of Groningen

   University of Groningen3-D interactive visualisation tools for Hi spectral line imagingvan der Hulst, J. M.; Punzo, D.; Roerdink, J. B. T. M.Published in:Proceedings of the International Astronomical Union, IAU SymposiumDOI:10.1017/S174392131700134XIMPORTANT NOTE: You are advised to consult the publisher's version (publisher's PDF) if you wish to cite fromit. Please check the document version below.Document VersionPublisher's PDF, also known as Version of recordPublication date:2016Link to publication in University of Groningen/UMCG research databaseCitation for published version (APA):van der Hulst, J. M., Punzo, D., & Roerdink, J. B. T. M. (2016). 3-D interactive visualisation tools for Hispectral line imaging. Proceedings of the International Astronomical Union, IAU Symposium, 325, 305-310.https://doi.org/10.1017/S174392131700134XCopyrightOther than for strictly personal use, it is not permitted to download or to forward/distribute the text or part of it without the consent of theauthor(s) and/or copyright holder(s), unless the work is under an open content license (like Creative Commons).The publication may also be distributed here under the terms of Article 25fa of the Dutch Copyright Act, indicated by the “Taverne” license.More information can be found on the University of Groningen website: https://www.rug.nl/library/open-access/self-archiving-pure/taverne-amendment.Take-down policyIf you believe that this document breaches copyright please contact us providing details, and we will remove access to the work immediatelyand investigate your claim.Downloaded from the University of Groningen/UMCG research database (Pure): http://www.rug.nl/research/portal. For technical reasons thenumber of authors shown on this cover page is limited to 10 maximum.Download date: 14-02-2023AstroinformaticsProceedings IAU Symposium No. 325, 2016M. Brescia, S.G. Djorgovski, E. Feigelson,G. Longo & S. Cavuoti, eds.c© International Astronomical Union 2017doi:10.1017/S174392131700134X3-D interactive visualisation tools for H Ispectral line imagingJ. M. van der Hulst, D. Punzo1 and J. B. T. M. Roerdink21Kapteyn Astronomical Institute, University of Groningen,Landleven 12, NL-9747AD, Groningen, the Netherlandsemail: j.m.van.der.hulst@rug.nlpunzo@astro.rug.nl2 Johann Bernoulli Institute for Mathematics and Computer Science, University of Groningen,Nijenborgh 9, NL-9747AG Groningen, the Netherlandsemail: j.b.t.m.roerdink@rug.nlAbstract. Upcoming H I surveys will deliver such large datasets that automated processingusing the full 3-D information to find and characterize H I objects is unavoidable. Full 3-Dvisualization is an essential tool for enabling qualitative and quantitative inspection and analysisof the 3-D data, which is often complex in nature. Here we present SlicerAstro, an open-sourceextension of 3DSlicer, a multi-platform open source software package for visualization andmedical image processing, which we developed for the inspection and analysis of H I spectral linedata. We describe its initial capabilities, including 3-D filtering, 3-D selection and comparativemodelling.Keywords. techniques: image processing; methods: data analysis; galaxies: structure, evolution,interactions, ISM1. IntroductionUpcoming H I surveys with instruments such as ASKAP (Australian Square KilometreArray Pathfinder, Johnston et al. 2008) and Apertif on the WSRT (Westerbork SynthesisRadio Telescope, Verheijen et al. 2009, Oosterloo et al. 2010) will provide large dataflows, in the regime of a few TB per day. Automation of the calibrating and imaging isimperative, and so is the automation of finding objects in the noisy H I data cubes. Theexpectation is that an H I survey with each of these instruments is capable of deliveringof the order of 100 objects per day (Duffy et al. 2012). Automated source finders suchas Duchamp (Whiting 2012) or SoFiA (Serra et al. 2015) will locate the detected objects.Yet in many cases, in particular when the objects are well resolved, further inspection isrequired to fully explore the characteristics of these object and interpret the subtle signsin the H I data of the various mechanisms of gas accretion and removal in galaxies (foran extended review of these see Sancisi et al. 2008).In the early 90’s it was realised that viewers for radio-astronomical spectral line datawould benefit greatly from advanced 3-D visualization software (Norris 1994). It was veryclear already then that full a 3-D approach would greatly help understanding of the 3-Dstructure of the radio data. The type of 2-D data slicing commonly used (i.e., channelmovies), and implemented in packages such as karma (Gooch 1996), is useful but limitedin that it misses the capability to get a full 3-D view of the data. Recently, both Hassan& Fluke (2011) and Koribalski (2012) pointed this out and and described need for 3-Dvisualisation capabilities in the context of upcoming H I surveys.305https://www.cambridge.org/core/terms. https://doi.org/10.1017/S174392131700134XDownloaded from https://www.cambridge.org/core. University of Groningen, on 18 Jan 2018 at 08:30:54, subject to the Cambridge Core terms of use, available at306 J. M. van der Hulst, D. Punzo & J. B. T. M. RoerdinkPunzo et al. (2015) discussed the requirements for a fully interactive visualization tool:coupled 1-D/2-D/3-D visualization, quantitative and comparative capabilities, combinedwith supervised semi-automated analysis. In addition the source code must be open, mod-ular, well documented, and well maintained. Punzo et al. (2015) reviewed several existingpackages and decided to develop a module for 3DSlicer (URL: https://www.slicer.org/)which adds the following characteristics to the already large number of capabilities ofthe current 3DSlicer system: (i) the ability to read FITS (Flexible Image TransportSystem, Wells et al. 1981) files and handle the astronomical world coordinates system(WCS, Calabretta & Greisen 2002, Greisen & Calabretta 2002, and Greisen et al. 2006),(ii) coupled 1-D, 2-D and 3-D display of the data and fast rendering, (iii) semi-automatedfiltering methods to recover faint emission using the information in all three dimensions(Punzo et al. 2016), (iv) 3-D selection and (v) use of 3-D modelling to characterise andselect the regularly rotating H I disks in galaxies. This paper gives a brief overview of thecapabilities of SlicerAstro. For a detailed description see Punzo et al. (2015), Punzoet al. (2016) and Punzo et al. (2017, in preparation).2. SlicerAstroSlicerAstro (URL: https://github.com/Punzo/SlicerAstro) is an open-source exten-sion of 3DSlicer, provided via its built-in extension manager. All functionality withinSlicerAstro is geared toward handling 3-D radio astronomical data, but makes use of allbuilt in features of the core software of 3DSlicer, fully exploiting the fast 3-D rendering,linked display capabilities and segmentation or region of interest selection. It will be ableto handle 3-D data cubes of op to 109 voxels on modern desktop machines which areusually with rather fast GPU’s. The present implementation of SlicerAstro providesthe following features that are of great interest for the exploration of radio astronomicalspectral line (in particular H I) data:(a) visualization of astronomical data cubes using the FITS data format;(b) active use of proper astronomical world coordinates and physical units;(c) interactive (semi)automatic smoothing in all three dimensions;(d) interactive 3-D selection of H I sources;(e) interactive H I data modelling coupled to visualization.Figure 1 shows the basic layout of SlicerAstro in the 3DSlicer environment. Theleft panel includes widgets for changing the colour transfer function for 2-D displays andfor changing the properties of the volume rendering, In addition it includes interfacewidgets to control the display proprieties (e.g., user interaction to rotate the 3-D view)and a window for displaying the properties of a data probe coupled to the cursor in thelinked 2-D views. The rest of the display in Figure 1 can be used to display the data in anumber of user specified ways, ranging from a single 3D view to a combination of linkedviews as presented here. In Figure 1 the arrangement of views consists of two linked 3Dviews of two different versions of the same data set (the original data to the left and thefiltered data to the right) at the top and three linked 2D views at the bottom displayingthe original data in three slices: right ascension versus declination, velocity versus rightascension and velocity versus declination. At the top of these three 2D displays there isa slide bar which allows moving through the third coordinate, i.e. velocity, declinationand right ascension respectively.https://www.cambridge.org/core/terms. https://doi.org/10.1017/S174392131700134XDownloaded from https://www.cambridge.org/core. University of Groningen, on 18 Jan 2018 at 08:30:54, subject to the Cambridge Core terms of use, available at3-D visualisation of H I data 307Figure 1. Screenshot of a full view of SlicerAstro in 3DSlicer. The left panel contains widgetswith various control functions described in the text, the right panel is a 5 frame layout showinglinked 3D views of two data sets at the top and three linked 2D views of the left data set atthe bottom. Additional labeling indicates the location of the extension manager control button(for adding packages) and the probe region (lower left) where the astronomical coordinates aredisplayed of the cursor position in the bottom 2D displays.2.1. FilteringIn order to find and enhance faint and extended features in H I data one usually lowersthe resolution in right ascension, declination and/or velocity to increase the signal-to-noise. The optimal shape of the smoothing kernel is of course the one that follows the3D structure of the faint features in the data such that it is just resolved in each of thethree dimensions. The exact shape is, however, not known a priory, so that in practiceone often uses a set of different kernels and then inspects the results to make a finaldecision about which one suits best.Punzo et al. (2016) have investigated this problem and showed that there is an optimumfiltering algorithm that works very effectively on H I data. This filter is an adaptive filter,based on the intensity-gradient driven filter of Perona & Malik (1990), which appearedto be quite effective on H I data with well chosen default parameters. This filter, alsoknown as anisotropic diffusion, does preserve the original resolution for regions wherethe signal-to-noise is high, but adapts to lower resolutions in regions where the signal-to-noise becomes very low but where there still is coherence in the data on larger spatialand/or velocity scales. This method has been implemented in SlicerAstro, in additionto the general Gaussian smoothing. The left 3D panel in Figure 1 shows a version ofthe high resolution data displayed at the left after application of this intensity-gradientdriven filter.2.2. 3-D data selectionHaving an optimized 3-D selection technique is still a problem that has not been solvedsatisfactorily, not only in astronomy, but also not in medical and other visualizationapplications. Moreover, the optimal selection technique highly depends on the naturehttps://www.cambridge.org/core/terms. https://doi.org/10.1017/S174392131700134XDownloaded from https://www.cambridge.org/core. University of Groningen, on 18 Jan 2018 at 08:30:54, subject to the Cambridge Core terms of use, available at308 J. M. van der Hulst, D. Punzo & J. B. T. M. RoerdinkFigure 2. Screenshot of the CloudLasso selection in SlicerAstro. The selection as drawn bythe user in the top right panel with the filtered data is indicated by the yellow line. The selectionis simultaneously displayed in the top left panel displaying the original, unfiltered data. For thefinal selection see Figure 3Figure 3. Screenshot showing three selections, highlighted in blue, ochre and red respectively,with the CloudLasso selection tool. The blue segmentation shows the selection in the CloudLassoregion indicated in Figure 2. The ochre and red segmentations are separate selections of thesecond galaxy and the H I structure between the galaxies respectively. Both the two top 3-Ddisplays and the bottom three 2-D displays show all three selections simultaneously. They canbe switched on or off separately.https://www.cambridge.org/core/terms. https://doi.org/10.1017/S174392131700134XDownloaded from https://www.cambridge.org/core. University of Groningen, on 18 Jan 2018 at 08:30:54, subject to the Cambridge Core terms of use, available at3-D visualisation of H I data 309of the data, i.e. the complexity of the 3-D data structure. Requirements for a 3-D se-lection tool are interactivity and minimal number of user-operations for achieving theselection (i.e. user-friendliness). Yu et al. (2012a) and Yu et al. (2012b) provide a reviewof the state-of-the-art 3-D selection algorithms. In SlicerAstro we implemented theCloudLasso technique Yu et al. (2012) for 3-D selection. The CloudLasso selection usesthe Marching Cubes (MC) algorithm (Wyvill et al. 1986, Lorensen & Cline 1987) for theidentification of regions of voxels with signal inside a user-drawn lasso, i. e. CloudLassois a lasso-constrained Marching Cubes method. The MC method allows spatial selectionof individual structures with a specified signal-to-noise ratio (usually  3) within a lassoregion, even if these lie visually one behind one another, without including the noisyregions in between. A detailed description of the CloudLasso selection can be found inPunzo et al. (2017, in preparation).Figure 2 shows a region selected with the CloudLasso. The selection in Figure 2 hasbeen done on the filtered version of the data (top right panel) and is indicated by theclosed yellow line. In this example the selection is automatically applied to the originaldata shown in the top left panel. The actual selection using the MC algorithm is shownin Figure 3 as the region highlighted in blue. The selection (defined as a segmentationstructure in 3DSlicer) is also shown in the three lower slices in Figure 3, highlightedin the same blue colour. In addition Figure 3 shows two other selections made withthe CloudLasso: a red selection around the faint H I bridge connecting the two galaxiesin this example and a ochre selection highlighting the second galaxy. Also these twoselections are shown in the three slices at the bottom. The CloudLasso implementationallows interactive modification of these selections.The selections are saved in 3DSlicer as segmentation structures, but can be exportedas FITS files in the form of masks. All voxels in such a FITS file are zeros, except forthe ones within a segmentation structure. These are numbered 1, 2, 3, etc. following thenumbering of the segmentations. Conversely a FITS file with masks, produced by anotherprogramme, e.g. SoFiA (Serra et al. 2015), can be read into SlicerAstro, be displayedand modified using the CloudLasso mechanism.2.3. Model aided selectionRather than selecting regions by hand, one can use a model representing the data tohighlight the parts of the 3-D structure that fit the model well and separate those fromthe regions in the 3-D data volume that do not fit the data. A simple example is thefollowing (see Figure 5 in Punzo et al. 2015): one can use a classical tilted ring modelto describe the regularly rotating H I disk of an observed galaxy and use this model tohighlight all parts of the data that fit the model well. All data that does not fit the model,i.e. deviates from regular rotation, can then easily be viewed as it will not be highlighted,i.e. have a contrasting colour. The full 3-D rendering capabilities of SlicerAstro thenallow the user to easily inspect the full 3-D structure of the H I that exhibits anomalousvelocities.This capability is being implemented in SlicerAstro as well, using the automated3-D fitting routine 3D Barolo (Di Teodoro & Fraternali 2015) for fitting the regularlyrotating H I disks in galaxies. This implementation provides both a (partly interactive)modelling capability and allows separating the H I with regular kinematics from the H Iwith anomalous kinematics in the data cubes of observed galaxies. The data used for themodel fitting can be preselected using the CloudLasso procedure described above.In this overview we have presented a new extension, SlicerAstro, that has beenadded to the existing 3DSlicer platform, developed originally for scientific visualisa-tion of medical data. SlicerAstro is not only capable of reading FITS format data,https://www.cambridge.org/core/terms. https://doi.org/10.1017/S174392131700134XDownloaded from https://www.cambridge.org/core. University of Groningen, on 18 Jan 2018 at 08:30:54, subject to the Cambridge Core terms of use, available at310 J. M. van der Hulst, D. Punzo & J. B. T. M. Roerdinkbut also understand and handles astronomical world coordinates and physical units.The first few capabilities implemented in SlicerAstro and described here are filtering,3-D selection and model aided exploration of H I data. The system is still under de-velopment and we expect to add more features in the future. User feedback is greatlyappreciated and can be directed to the first two authors of this paper or be posted onhttps://github.com/Punzo/SlicerAstro.3. AcknowledgmentsD. Punzo and J.M van der Hulst gratefully acknowledge support from the European Re-search Council under the European Union’s Seventh Framework Programme (FP/2007-2013)/ERC Grant Agreement nr. 291531.ReferencesCalabretta, M. R. & Greisen, E. W. 2002, A&A, 395, 1077Duffy, A. R., Meyer, M. J., Staveley-Smith, L., et al. 2012, MNRAS, 426, 3385Di Teodoro, E. M. & Fraternali, F. 2015, MNRAS 451, 3021Gooch, R. 1996, Astronomical Data Analysis Software and Systems V, 101, 80Greisen, E. W. & Calabretta, M. R. 2002, A&A, 395, 1061Greisen, E. W., Calabretta, M. R., Valdes, F. G., & Allen, S. L. 2006, A&A, 446, 747Hassan, A. & Fluke, C. J. 2011, Publications of the Astronomical Society of Australia, 28, 150Johnston, S., Taylor, R., Bailes, M., et al. 2008, Experimental Astronomy, 22, 151Koribalski, B. S. 2012, Publications of the Astronomical Society of Australia, 29, 359Lorensen, W. E. & Cline, H. E. 1987, SIGGRAPH Comput. Graph. vol. 21, nr. 4, 163Norris, R. P. 1994, Astronomical Data Analysis Software and Systems III, 61, 51Oosterloo, T., Verheijen, M., & van Cappellen, W. 2010, ISKAF2010 Science Meeting, 43Perona, P & Malik, J. 1990, IEEE, 12, 629Punzo, D., van der Hulst, J. M., & Roerdink, J. B. T. M. 2016, Astronomy and Computing, 17,163Punzo, D., van der Hulst, J. M., Roerdink, J. B. T. M., et al. 2015, Astronomy and Computing,12, 86Sancisi, R., Fraternali, F., Oosterloo, T., & van der Hulst, T. 2008, A&A Review, 15, 189Serra, P., Westmeier, T., Giese, N., et al. 2015, MNRAS, 448, 1922Verheijen, M., Oosterloo, T., Heald, G., & van Cappellen, W. 2009, Panoramic Radio Astronomy:Wide-field 1-2 GHz Research on Galaxy Evolution, 10Wells, D. C., Greisen, E. W., & Harten, R. H. 1981, A&A Suppl., 44, 363Whiting, M. T. 2012, MNRAS, 421, 3242Wyvill, G., McPheeters, C., & Wyvill, B. 1986, The Visual Computer, vol. 2, nr. 4, 227Yu, L., Efstatiou, K., Isenberg, P., & Isenberg, T. 2012, IEEE Transactions on Visualizationand Computer Graphics, vol. 18, nr. 12, 2245Yu, L., Efstatiou, K., Isenberg, P., & Isenberg, T. 2012, IEEE Transactions on Visualizationand Computer Graphics, vol. 22, nr. 1, 1077https://www.cambridge.org/core/terms. https://doi.org/10.1017/S174392131700134XDownloaded from https://www.cambridge.org/core. University of Groningen, on 18 Jan 2018 at 08:30:54, subject to the Cambridge Core terms of use, available at

   University of Groningen3-D interactive visualisation tools for Hi spectral line imagingvan der Hulst, J. M.; Punzo, D.; Roerdink, J. B. T. M.Published in:Proceedings of the International Astronomical Union, IAU SymposiumDOI:10.1017/S174392131700134XIMPORTANT NOTE: You are advised to consult the publisher's version (publisher's PDF) if you wish to cite fromit. Please check the document version below.Document VersionPublisher's PDF, also known as Version of recordPublication date:2016Link to publication in University of Groningen/UMCG research databaseCitation for published version (APA):van der Hulst, J. M., Punzo, D., & Roerdink, J. B. T. M. (2016). 3-D interactive visualisation tools for Hispectral line imaging. Proceedings of the International Astronomical Union, IAU Symposium, 325, 305-310.https://doi.org/10.1017/S174392131700134XCopyrightOther than for strictly personal use, it is not permitted to download or to forward/distribute the text or part of it without the consent of theauthor(s) and/or copyright holder(s), unless the work is under an open content license (like Creative Commons).The publication may also be distributed here under the terms of Article 25fa of the Dutch Copyright Act, indicated by the “Taverne” license.More information can be found on the University of Groningen website: https://www.rug.nl/library/open-access/self-archiving-pure/taverne-amendment.Take-down policyIf you believe that this document breaches copyright please contact us providing details, and we will remove access to the work immediatelyand investigate your claim.Downloaded from the University of Groningen/UMCG research database (Pure): http://www.rug.nl/research/portal. For technical reasons thenumber of authors shown on this cover page is limited to 10 maximum.Download date: 19-11-2022AstroinformaticsProceedings IAU Symposium No. 325, 2016M. Brescia, S.G. Djorgovski, E. Feigelson,G. Longo & S. Cavuoti, eds.c© International Astronomical Union 2017doi:10.1017/S174392131700134X3-D interactive visualisation tools for H Ispectral line imagingJ. M. van der Hulst, D. Punzo1 and J. B. T. M. Roerdink21Kapteyn Astronomical Institute, University of Groningen,Landleven 12, NL-9747AD, Groningen, the Netherlandsemail: j.m.van.der.hulst@rug.nlpunzo@astro.rug.nl2 Johann Bernoulli Institute for Mathematics and Computer Science, University of Groningen,Nijenborgh 9, NL-9747AG Groningen, the Netherlandsemail: j.b.t.m.roerdink@rug.nlAbstract. Upcoming H I surveys will deliver such large datasets that automated processingusing the full 3-D information to find and characterize H I objects is unavoidable. Full 3-Dvisualization is an essential tool for enabling qualitative and quantitative inspection and analysisof the 3-D data, which is often complex in nature. Here we present SlicerAstro, an open-sourceextension of 3DSlicer, a multi-platform open source software package for visualization andmedical image processing, which we developed for the inspection and analysis of H I spectral linedata. We describe its initial capabilities, including 3-D filtering, 3-D selection and comparativemodelling.Keywords. techniques: image processing; methods: data analysis; galaxies: structure, evolution,interactions, ISM1. IntroductionUpcoming H I surveys with instruments such as ASKAP (Australian Square KilometreArray Pathfinder, Johnston et al. 2008) and Apertif on the WSRT (Westerbork SynthesisRadio Telescope, Verheijen et al. 2009, Oosterloo et al. 2010) will provide large dataflows, in the regime of a few TB per day. Automation of the calibrating and imaging isimperative, and so is the automation of finding objects in the noisy H I data cubes. Theexpectation is that an H I survey with each of these instruments is capable of deliveringof the order of 100 objects per day (Duffy et al. 2012). Automated source finders suchas Duchamp (Whiting 2012) or SoFiA (Serra et al. 2015) will locate the detected objects.Yet in many cases, in particular when the objects are well resolved, further inspection isrequired to fully explore the characteristics of these object and interpret the subtle signsin the H I data of the various mechanisms of gas accretion and removal in galaxies (foran extended review of these see Sancisi et al. 2008).In the early 90’s it was realised that viewers for radio-astronomical spectral line datawould benefit greatly from advanced 3-D visualization software (Norris 1994). It was veryclear already then that full a 3-D approach would greatly help understanding of the 3-Dstructure of the radio data. The type of 2-D data slicing commonly used (i.e., channelmovies), and implemented in packages such as karma (Gooch 1996), is useful but limitedin that it misses the capability to get a full 3-D view of the data. Recently, both Hassan& Fluke (2011) and Koribalski (2012) pointed this out and and described need for 3-Dvisualisation capabilities in the context of upcoming H I surveys.305https://www.cambridge.org/core/terms. https://doi.org/10.1017/S174392131700134XDownloaded from https://www.cambridge.org/core. University of Groningen, on 18 Jan 2018 at 08:30:54, subject to the Cambridge Core terms of use, available at306 J. M. van der Hulst, D. Punzo & J. B. T. M. RoerdinkPunzo et al. (2015) discussed the requirements for a fully interactive visualization tool:coupled 1-D/2-D/3-D visualization, quantitative and comparative capabilities, combinedwith supervised semi-automated analysis. In addition the source code must be open, mod-ular, well documented, and well maintained. Punzo et al. (2015) reviewed several existingpackages and decided to develop a module for 3DSlicer (URL: https://www.slicer.org/)which adds the following characteristics to the already large number of capabilities ofthe current 3DSlicer system: (i) the ability to read FITS (Flexible Image TransportSystem, Wells et al. 1981) files and handle the astronomical world coordinates system(WCS, Calabretta & Greisen 2002, Greisen & Calabretta 2002, and Greisen et al. 2006),(ii) coupled 1-D, 2-D and 3-D display of the data and fast rendering, (iii) semi-automatedfiltering methods to recover faint emission using the information in all three dimensions(Punzo et al. 2016), (iv) 3-D selection and (v) use of 3-D modelling to characterise andselect the regularly rotating H I disks in galaxies. This paper gives a brief overview of thecapabilities of SlicerAstro. For a detailed description see Punzo et al. (2015), Punzoet al. (2016) and Punzo et al. (2017, in preparation).2. SlicerAstroSlicerAstro (URL: https://github.com/Punzo/SlicerAstro) is an open-source exten-sion of 3DSlicer, provided via its built-in extension manager. All functionality withinSlicerAstro is geared toward handling 3-D radio astronomical data, but makes use of allbuilt in features of the core software of 3DSlicer, fully exploiting the fast 3-D rendering,linked display capabilities and segmentation or region of interest selection. It will be ableto handle 3-D data cubes of op to 109 voxels on modern desktop machines which areusually with rather fast GPU’s. The present implementation of SlicerAstro providesthe following features that are of great interest for the exploration of radio astronomicalspectral line (in particular H I) data:(a) visualization of astronomical data cubes using the FITS data format;(b) active use of proper astronomical world coordinates and physical units;(c) interactive (semi)automatic smoothing in all three dimensions;(d) interactive 3-D selection of H I sources;(e) interactive H I data modelling coupled to visualization.Figure 1 shows the basic layout of SlicerAstro in the 3DSlicer environment. Theleft panel includes widgets for changing the colour transfer function for 2-D displays andfor changing the properties of the volume rendering, In addition it includes interfacewidgets to control the display proprieties (e.g., user interaction to rotate the 3-D view)and a window for displaying the properties of a data probe coupled to the cursor in thelinked 2-D views. The rest of the display in Figure 1 can be used to display the data in anumber of user specified ways, ranging from a single 3D view to a combination of linkedviews as presented here. In Figure 1 the arrangement of views consists of two linked 3Dviews of two different versions of the same data set (the original data to the left and thefiltered data to the right) at the top and three linked 2D views at the bottom displayingthe original data in three slices: right ascension versus declination, velocity versus rightascension and velocity versus declination. At the top of these three 2D displays there isa slide bar which allows moving through the third coordinate, i.e. velocity, declinationand right ascension respectively.https://www.cambridge.org/core/terms. https://doi.org/10.1017/S174392131700134XDownloaded from https://www.cambridge.org/core. University of Groningen, on 18 Jan 2018 at 08:30:54, subject to the Cambridge Core terms of use, available at3-D visualisation of H I data 307Figure 1. Screenshot of a full view of SlicerAstro in 3DSlicer. The left panel contains widgetswith various control functions described in the text, the right panel is a 5 frame layout showinglinked 3D views of two data sets at the top and three linked 2D views of the left data set atthe bottom. Additional labeling indicates the location of the extension manager control button(for adding packages) and the probe region (lower left) where the astronomical coordinates aredisplayed of the cursor position in the bottom 2D displays.2.1. FilteringIn order to find and enhance faint and extended features in H I data one usually lowersthe resolution in right ascension, declination and/or velocity to increase the signal-to-noise. The optimal shape of the smoothing kernel is of course the one that follows the3D structure of the faint features in the data such that it is just resolved in each of thethree dimensions. The exact shape is, however, not known a priory, so that in practiceone often uses a set of different kernels and then inspects the results to make a finaldecision about which one suits best.Punzo et al. (2016) have investigated this problem and showed that there is an optimumfiltering algorithm that works very effectively on H I data. This filter is an adaptive filter,based on the intensity-gradient driven filter of Perona & Malik (1990), which appearedto be quite effective on H I data with well chosen default parameters. This filter, alsoknown as anisotropic diffusion, does preserve the original resolution for regions wherethe signal-to-noise is high, but adapts to lower resolutions in regions where the signal-to-noise becomes very low but where there still is coherence in the data on larger spatialand/or velocity scales. This method has been implemented in SlicerAstro, in additionto the general Gaussian smoothing. The left 3D panel in Figure 1 shows a version ofthe high resolution data displayed at the left after application of this intensity-gradientdriven filter.2.2. 3-D data selectionHaving an optimized 3-D selection technique is still a problem that has not been solvedsatisfactorily, not only in astronomy, but also not in medical and other visualizationapplications. Moreover, the optimal selection technique highly depends on the naturehttps://www.cambridge.org/core/terms. https://doi.org/10.1017/S174392131700134XDownloaded from https://www.cambridge.org/core. University of Groningen, on 18 Jan 2018 at 08:30:54, subject to the Cambridge Core terms of use, available at308 J. M. van der Hulst, D. Punzo & J. B. T. M. RoerdinkFigure 2. Screenshot of the CloudLasso selection in SlicerAstro. The selection as drawn bythe user in the top right panel with the filtered data is indicated by the yellow line. The selectionis simultaneously displayed in the top left panel displaying the original, unfiltered data. For thefinal selection see Figure 3Figure 3. Screenshot showing three selections, highlighted in blue, ochre and red respectively,with the CloudLasso selection tool. The blue segmentation shows the selection in the CloudLassoregion indicated in Figure 2. The ochre and red segmentations are separate selections of thesecond galaxy and the H I structure between the galaxies respectively. Both the two top 3-Ddisplays and the bottom three 2-D displays show all three selections simultaneously. They canbe switched on or off separately.https://www.cambridge.org/core/terms. https://doi.org/10.1017/S174392131700134XDownloaded from https://www.cambridge.org/core. University of Groningen, on 18 Jan 2018 at 08:30:54, subject to the Cambridge Core terms of use, available at3-D visualisation of H I data 309of the data, i.e. the complexity of the 3-D data structure. Requirements for a 3-D se-lection tool are interactivity and minimal number of user-operations for achieving theselection (i.e. user-friendliness). Yu et al. (2012a) and Yu et al. (2012b) provide a reviewof the state-of-the-art 3-D selection algorithms. In SlicerAstro we implemented theCloudLasso technique Yu et al. (2012) for 3-D selection. The CloudLasso selection usesthe Marching Cubes (MC) algorithm (Wyvill et al. 1986, Lorensen & Cline 1987) for theidentification of regions of voxels with signal inside a user-drawn lasso, i. e. CloudLassois a lasso-constrained Marching Cubes method. The MC method allows spatial selectionof individual structures with a specified signal-to-noise ratio (usually  3) within a lassoregion, even if these lie visually one behind one another, without including the noisyregions in between. A detailed description of the CloudLasso selection can be found inPunzo et al. (2017, in preparation).Figure 2 shows a region selected with the CloudLasso. The selection in Figure 2 hasbeen done on the filtered version of the data (top right panel) and is indicated by theclosed yellow line. In this example the selection is automatically applied to the originaldata shown in the top left panel. The actual selection using the MC algorithm is shownin Figure 3 as the region highlighted in blue. The selection (defined as a segmentationstructure in 3DSlicer) is also shown in the three lower slices in Figure 3, highlightedin the same blue colour. In addition Figure 3 shows two other selections made withthe CloudLasso: a red selection around the faint H I bridge connecting the two galaxiesin this example and a ochre selection highlighting the second galaxy. Also these twoselections are shown in the three slices at the bottom. The CloudLasso implementationallows interactive modification of these selections.The selections are saved in 3DSlicer as segmentation structures, but can be exportedas FITS files in the form of masks. All voxels in such a FITS file are zeros, except forthe ones within a segmentation structure. These are numbered 1, 2, 3, etc. following thenumbering of the segmentations. Conversely a FITS file with masks, produced by anotherprogramme, e.g. SoFiA (Serra et al. 2015), can be read into SlicerAstro, be displayedand modified using the CloudLasso mechanism.2.3. Model aided selectionRather than selecting regions by hand, one can use a model representing the data tohighlight the parts of the 3-D structure that fit the model well and separate those fromthe regions in the 3-D data volume that do not fit the data. A simple example is thefollowing (see Figure 5 in Punzo et al. 2015): one can use a classical tilted ring modelto describe the regularly rotating H I disk of an observed galaxy and use this model tohighlight all parts of the data that fit the model well. All data that does not fit the model,i.e. deviates from regular rotation, can then easily be viewed as it will not be highlighted,i.e. have a contrasting colour. The full 3-D rendering capabilities of SlicerAstro thenallow the user to easily inspect the full 3-D structure of the H I that exhibits anomalousvelocities.This capability is being implemented in SlicerAstro as well, using the automated3-D fitting routine 3D Barolo (Di Teodoro & Fraternali 2015) for fitting the regularlyrotating H I disks in galaxies. This implementation provides both a (partly interactive)modelling capability and allows separating the H I with regular kinematics from the H Iwith anomalous kinematics in the data cubes of observed galaxies. The data used for themodel fitting can be preselected using the CloudLasso procedure described above.In this overview we have presented a new extension, SlicerAstro, that has beenadded to the existing 3DSlicer platform, developed originally for scientific visualisa-tion of medical data. SlicerAstro is not only capable of reading FITS format data,https://www.cambridge.org/core/terms. https://doi.org/10.1017/S174392131700134XDownloaded from https://www.cambridge.org/core. University of Groningen, on 18 Jan 2018 at 08:30:54, subject to the Cambridge Core terms of use, available at310 J. M. van der Hulst, D. Punzo & J. B. T. M. Roerdinkbut also understand and handles astronomical world coordinates and physical units.The first few capabilities implemented in SlicerAstro and described here are filtering,3-D selection and model aided exploration of H I data. The system is still under de-velopment and we expect to add more features in the future. User feedback is greatlyappreciated and can be directed to the first two authors of this paper or be posted onhttps://github.com/Punzo/SlicerAstro.3. AcknowledgmentsD. Punzo and J.M van der Hulst gratefully acknowledge support from the European Re-search Council under the European Union’s Seventh Framework Programme (FP/2007-2013)/ERC Grant Agreement nr. 291531.ReferencesCalabretta, M. R. & Greisen, E. W. 2002, A&A, 395, 1077Duffy, A. R., Meyer, M. J., Staveley-Smith, L., et al. 2012, MNRAS, 426, 3385Di Teodoro, E. M. & Fraternali, F. 2015, MNRAS 451, 3021Gooch, R. 1996, Astronomical Data Analysis Software and Systems V, 101, 80Greisen, E. W. & Calabretta, M. R. 2002, A&A, 395, 1061Greisen, E. W., Calabretta, M. R., Valdes, F. G., & Allen, S. L. 2006, A&A, 446, 747Hassan, A. & Fluke, C. J. 2011, Publications of the Astronomical Society of Australia, 28, 150Johnston, S., Taylor, R., Bailes, M., et al. 2008, Experimental Astronomy, 22, 151Koribalski, B. S. 2012, Publications of the Astronomical Society of Australia, 29, 359Lorensen, W. E. & Cline, H. E. 1987, SIGGRAPH Comput. Graph. vol. 21, nr. 4, 163Norris, R. P. 1994, Astronomical Data Analysis Software and Systems III, 61, 51Oosterloo, T., Verheijen, M., & van Cappellen, W. 2010, ISKAF2010 Science Meeting, 43Perona, P & Malik, J. 1990, IEEE, 12, 629Punzo, D., van der Hulst, J. M., & Roerdink, J. B. T. M. 2016, Astronomy and Computing, 17,163Punzo, D., van der Hulst, J. M., Roerdink, J. B. T. M., et al. 2015, Astronomy and Computing,12, 86Sancisi, R., Fraternali, F., Oosterloo, T., & van der Hulst, T. 2008, A&A Review, 15, 189Serra, P., Westmeier, T., Giese, N., et al. 2015, MNRAS, 448, 1922Verheijen, M., Oosterloo, T., Heald, G., & van Cappellen, W. 2009, Panoramic Radio Astronomy:Wide-field 1-2 GHz Research on Galaxy Evolution, 10Wells, D. C., Greisen, E. W., & Harten, R. H. 1981, A&A Suppl., 44, 363Whiting, M. T. 2012, MNRAS, 421, 3242Wyvill, G., McPheeters, C., & Wyvill, B. 1986, The Visual Computer, vol. 2, nr. 4, 227Yu, L., Efstatiou, K., Isenberg, P., & Isenberg, T. 2012, IEEE Transactions on Visualizationand Computer Graphics, vol. 18, nr. 12, 2245Yu, L., Efstatiou, K., Isenberg, P., & Isenberg, T. 2012, IEEE Transactions on Visualizationand Computer Graphics, vol. 22, nr. 1, 1077https://www.cambridge.org/core/terms. https://doi.org/10.1017/S174392131700134XDownloaded from https://www.cambridge.org/core. University of Groningen, on 18 Jan 2018 at 08:30:54, subject to the Cambridge Core terms of use, available at

   University of Groningen3-D interactive visualisation tools for Hi spectral line imagingvan der Hulst, J. M.; Punzo, D.; Roerdink, J. B. T. M.Published in:Proceedings of the International Astronomical Union, IAU SymposiumDOI:10.1017/S174392131700134XIMPORTANT NOTE: You are advised to consult the publisher's version (publisher's PDF) if you wish to cite fromit. Please check the document version below.Document VersionPublisher's PDF, also known as Version of recordPublication date:2016Link to publication in University of Groningen/UMCG research databaseCitation for published version (APA):van der Hulst, J. M., Punzo, D., & Roerdink, J. B. T. M. (2016). 3-D interactive visualisation tools for Hispectral line imaging. Proceedings of the International Astronomical Union, IAU Symposium, 325, 305-310.https://doi.org/10.1017/S174392131700134XCopyrightOther than for strictly personal use, it is not permitted to download or to forward/distribute the text or part of it without the consent of theauthor(s) and/or copyright holder(s), unless the work is under an open content license (like Creative Commons).The publication may also be distributed here under the terms of Article 25fa of the Dutch Copyright Act, indicated by the “Taverne” license.More information can be found on the University of Groningen website: https://www.rug.nl/library/open-access/self-archiving-pure/taverne-amendment.Take-down policyIf you believe that this document breaches copyright please contact us providing details, and we will remove access to the work immediatelyand investigate your claim.Downloaded from the University of Groningen/UMCG research database (Pure): http://www.rug.nl/research/portal. For technical reasons thenumber of authors shown on this cover page is limited to 10 maximum.Download date: 10-09-2023AstroinformaticsProceedings IAU Symposium No. 325, 2016M. Brescia, S.G. Djorgovski, E. Feigelson,G. Longo & S. Cavuoti, eds.c© International Astronomical Union 2017doi:10.1017/S174392131700134X3-D interactive visualisation tools for H Ispectral line imagingJ. M. van der Hulst, D. Punzo1 and J. B. T. M. Roerdink21Kapteyn Astronomical Institute, University of Groningen,Landleven 12, NL-9747AD, Groningen, the Netherlandsemail: j.m.van.der.hulst@rug.nlpunzo@astro.rug.nl2 Johann Bernoulli Institute for Mathematics and Computer Science, University of Groningen,Nijenborgh 9, NL-9747AG Groningen, the Netherlandsemail: j.b.t.m.roerdink@rug.nlAbstract. Upcoming H I surveys will deliver such large datasets that automated processingusing the full 3-D information to find and characterize H I objects is unavoidable. Full 3-Dvisualization is an essential tool for enabling qualitative and quantitative inspection and analysisof the 3-D data, which is often complex in nature. Here we present SlicerAstro, an open-sourceextension of 3DSlicer, a multi-platform open source software package for visualization andmedical image processing, which we developed for the inspection and analysis of H I spectral linedata. We describe its initial capabilities, including 3-D filtering, 3-D selection and comparativemodelling.Keywords. techniques: image processing; methods: data analysis; galaxies: structure, evolution,interactions, ISM1. IntroductionUpcoming H I surveys with instruments such as ASKAP (Australian Square KilometreArray Pathfinder, Johnston et al. 2008) and Apertif on the WSRT (Westerbork SynthesisRadio Telescope, Verheijen et al. 2009, Oosterloo et al. 2010) will provide large dataflows, in the regime of a few TB per day. Automation of the calibrating and imaging isimperative, and so is the automation of finding objects in the noisy H I data cubes. Theexpectation is that an H I survey with each of these instruments is capable of deliveringof the order of 100 objects per day (Duffy et al. 2012). Automated source finders suchas Duchamp (Whiting 2012) or SoFiA (Serra et al. 2015) will locate the detected objects.Yet in many cases, in particular when the objects are well resolved, further inspection isrequired to fully explore the characteristics of these object and interpret the subtle signsin the H I data of the various mechanisms of gas accretion and removal in galaxies (foran extended review of these see Sancisi et al. 2008).In the early 90’s it was realised that viewers for radio-astronomical spectral line datawould benefit greatly from advanced 3-D visualization software (Norris 1994). It was veryclear already then that full a 3-D approach would greatly help understanding of the 3-Dstructure of the radio data. The type of 2-D data slicing commonly used (i.e., channelmovies), and implemented in packages such as karma (Gooch 1996), is useful but limitedin that it misses the capability to get a full 3-D view of the data. Recently, both Hassan& Fluke (2011) and Koribalski (2012) pointed this out and and described need for 3-Dvisualisation capabilities in the context of upcoming H I surveys.305https://www.cambridge.org/core/terms. https://doi.org/10.1017/S174392131700134XDownloaded from https://www.cambridge.org/core. University of Groningen, on 18 Jan 2018 at 08:30:54, subject to the Cambridge Core terms of use, available at306 J. M. van der Hulst, D. Punzo & J. B. T. M. RoerdinkPunzo et al. (2015) discussed the requirements for a fully interactive visualization tool:coupled 1-D/2-D/3-D visualization, quantitative and comparative capabilities, combinedwith supervised semi-automated analysis. In addition the source code must be open, mod-ular, well documented, and well maintained. Punzo et al. (2015) reviewed several existingpackages and decided to develop a module for 3DSlicer (URL: https://www.slicer.org/)which adds the following characteristics to the already large number of capabilities ofthe current 3DSlicer system: (i) the ability to read FITS (Flexible Image TransportSystem, Wells et al. 1981) files and handle the astronomical world coordinates system(WCS, Calabretta & Greisen 2002, Greisen & Calabretta 2002, and Greisen et al. 2006),(ii) coupled 1-D, 2-D and 3-D display of the data and fast rendering, (iii) semi-automatedfiltering methods to recover faint emission using the information in all three dimensions(Punzo et al. 2016), (iv) 3-D selection and (v) use of 3-D modelling to characterise andselect the regularly rotating H I disks in galaxies. This paper gives a brief overview of thecapabilities of SlicerAstro. For a detailed description see Punzo et al. (2015), Punzoet al. (2016) and Punzo et al. (2017, in preparation).2. SlicerAstroSlicerAstro (URL: https://github.com/Punzo/SlicerAstro) is an open-source exten-sion of 3DSlicer, provided via its built-in extension manager. All functionality withinSlicerAstro is geared toward handling 3-D radio astronomical data, but makes use of allbuilt in features of the core software of 3DSlicer, fully exploiting the fast 3-D rendering,linked display capabilities and segmentation or region of interest selection. It will be ableto handle 3-D data cubes of op to 109 voxels on modern desktop machines which areusually with rather fast GPU’s. The present implementation of SlicerAstro providesthe following features that are of great interest for the exploration of radio astronomicalspectral line (in particular H I) data:(a) visualization of astronomical data cubes using the FITS data format;(b) active use of proper astronomical world coordinates and physical units;(c) interactive (semi)automatic smoothing in all three dimensions;(d) interactive 3-D selection of H I sources;(e) interactive H I data modelling coupled to visualization.Figure 1 shows the basic layout of SlicerAstro in the 3DSlicer environment. Theleft panel includes widgets for changing the colour transfer function for 2-D displays andfor changing the properties of the volume rendering, In addition it includes interfacewidgets to control the display proprieties (e.g., user interaction to rotate the 3-D view)and a window for displaying the properties of a data probe coupled to the cursor in thelinked 2-D views. The rest of the display in Figure 1 can be used to display the data in anumber of user specified ways, ranging from a single 3D view to a combination of linkedviews as presented here. In Figure 1 the arrangement of views consists of two linked 3Dviews of two different versions of the same data set (the original data to the left and thefiltered data to the right) at the top and three linked 2D views at the bottom displayingthe original data in three slices: right ascension versus declination, velocity versus rightascension and velocity versus declination. At the top of these three 2D displays there isa slide bar which allows moving through the third coordinate, i.e. velocity, declinationand right ascension respectively.https://www.cambridge.org/core/terms. https://doi.org/10.1017/S174392131700134XDownloaded from https://www.cambridge.org/core. University of Groningen, on 18 Jan 2018 at 08:30:54, subject to the Cambridge Core terms of use, available at3-D visualisation of H I data 307Figure 1. Screenshot of a full view of SlicerAstro in 3DSlicer. The left panel contains widgetswith various control functions described in the text, the right panel is a 5 frame layout showinglinked 3D views of two data sets at the top and three linked 2D views of the left data set atthe bottom. Additional labeling indicates the location of the extension manager control button(for adding packages) and the probe region (lower left) where the astronomical coordinates aredisplayed of the cursor position in the bottom 2D displays.2.1. FilteringIn order to find and enhance faint and extended features in H I data one usually lowersthe resolution in right ascension, declination and/or velocity to increase the signal-to-noise. The optimal shape of the smoothing kernel is of course the one that follows the3D structure of the faint features in the data such that it is just resolved in each of thethree dimensions. The exact shape is, however, not known a priory, so that in practiceone often uses a set of different kernels and then inspects the results to make a finaldecision about which one suits best.Punzo et al. (2016) have investigated this problem and showed that there is an optimumfiltering algorithm that works very effectively on H I data. This filter is an adaptive filter,based on the intensity-gradient driven filter of Perona & Malik (1990), which appearedto be quite effective on H I data with well chosen default parameters. This filter, alsoknown as anisotropic diffusion, does preserve the original resolution for regions wherethe signal-to-noise is high, but adapts to lower resolutions in regions where the signal-to-noise becomes very low but where there still is coherence in the data on larger spatialand/or velocity scales. This method has been implemented in SlicerAstro, in additionto the general Gaussian smoothing. The left 3D panel in Figure 1 shows a version ofthe high resolution data displayed at the left after application of this intensity-gradientdriven filter.2.2. 3-D data selectionHaving an optimized 3-D selection technique is still a problem that has not been solvedsatisfactorily, not only in astronomy, but also not in medical and other visualizationapplications. Moreover, the optimal selection technique highly depends on the naturehttps://www.cambridge.org/core/terms. https://doi.org/10.1017/S174392131700134XDownloaded from https://www.cambridge.org/core. University of Groningen, on 18 Jan 2018 at 08:30:54, subject to the Cambridge Core terms of use, available at308 J. M. van der Hulst, D. Punzo & J. B. T. M. RoerdinkFigure 2. Screenshot of the CloudLasso selection in SlicerAstro. The selection as drawn bythe user in the top right panel with the filtered data is indicated by the yellow line. The selectionis simultaneously displayed in the top left panel displaying the original, unfiltered data. For thefinal selection see Figure 3Figure 3. Screenshot showing three selections, highlighted in blue, ochre and red respectively,with the CloudLasso selection tool. The blue segmentation shows the selection in the CloudLassoregion indicated in Figure 2. The ochre and red segmentations are separate selections of thesecond galaxy and the H I structure between the galaxies respectively. Both the two top 3-Ddisplays and the bottom three 2-D displays show all three selections simultaneously. They canbe switched on or off separately.https://www.cambridge.org/core/terms. https://doi.org/10.1017/S174392131700134XDownloaded from https://www.cambridge.org/core. University of Groningen, on 18 Jan 2018 at 08:30:54, subject to the Cambridge Core terms of use, available at3-D visualisation of H I data 309of the data, i.e. the complexity of the 3-D data structure. Requirements for a 3-D se-lection tool are interactivity and minimal number of user-operations for achieving theselection (i.e. user-friendliness). Yu et al. (2012a) and Yu et al. (2012b) provide a reviewof the state-of-the-art 3-D selection algorithms. In SlicerAstro we implemented theCloudLasso technique Yu et al. (2012) for 3-D selection. The CloudLasso selection usesthe Marching Cubes (MC) algorithm (Wyvill et al. 1986, Lorensen & Cline 1987) for theidentification of regions of voxels with signal inside a user-drawn lasso, i. e. CloudLassois a lasso-constrained Marching Cubes method. The MC method allows spatial selectionof individual structures with a specified signal-to-noise ratio (usually  3) within a lassoregion, even if these lie visually one behind one another, without including the noisyregions in between. A detailed description of the CloudLasso selection can be found inPunzo et al. (2017, in preparation).Figure 2 shows a region selected with the CloudLasso. The selection in Figure 2 hasbeen done on the filtered version of the data (top right panel) and is indicated by theclosed yellow line. In this example the selection is automatically applied to the originaldata shown in the top left panel. The actual selection using the MC algorithm is shownin Figure 3 as the region highlighted in blue. The selection (defined as a segmentationstructure in 3DSlicer) is also shown in the three lower slices in Figure 3, highlightedin the same blue colour. In addition Figure 3 shows two other selections made withthe CloudLasso: a red selection around the faint H I bridge connecting the two galaxiesin this example and a ochre selection highlighting the second galaxy. Also these twoselections are shown in the three slices at the bottom. The CloudLasso implementationallows interactive modification of these selections.The selections are saved in 3DSlicer as segmentation structures, but can be exportedas FITS files in the form of masks. All voxels in such a FITS file are zeros, except forthe ones within a segmentation structure. These are numbered 1, 2, 3, etc. following thenumbering of the segmentations. Conversely a FITS file with masks, produced by anotherprogramme, e.g. SoFiA (Serra et al. 2015), can be read into SlicerAstro, be displayedand modified using the CloudLasso mechanism.2.3. Model aided selectionRather than selecting regions by hand, one can use a model representing the data tohighlight the parts of the 3-D structure that fit the model well and separate those fromthe regions in the 3-D data volume that do not fit the data. A simple example is thefollowing (see Figure 5 in Punzo et al. 2015): one can use a classical tilted ring modelto describe the regularly rotating H I disk of an observed galaxy and use this model tohighlight all parts of the data that fit the model well. All data that does not fit the model,i.e. deviates from regular rotation, can then easily be viewed as it will not be highlighted,i.e. have a contrasting colour. The full 3-D rendering capabilities of SlicerAstro thenallow the user to easily inspect the full 3-D structure of the H I that exhibits anomalousvelocities.This capability is being implemented in SlicerAstro as well, using the automated3-D fitting routine 3D Barolo (Di Teodoro & Fraternali 2015) for fitting the regularlyrotating H I disks in galaxies. This implementation provides both a (partly interactive)modelling capability and allows separating the H I with regular kinematics from the H Iwith anomalous kinematics in the data cubes of observed galaxies. The data used for themodel fitting can be preselected using the CloudLasso procedure described above.In this overview we have presented a new extension, SlicerAstro, that has beenadded to the existing 3DSlicer platform, developed originally for scientific visualisa-tion of medical data. SlicerAstro is not only capable of reading FITS format data,https://www.cambridge.org/core/terms. https://doi.org/10.1017/S174392131700134XDownloaded from https://www.cambridge.org/core. University of Groningen, on 18 Jan 2018 at 08:30:54, subject to the Cambridge Core terms of use, available at310 J. M. van der Hulst, D. Punzo & J. B. T. M. Roerdinkbut also understand and handles astronomical world coordinates and physical units.The first few capabilities implemented in SlicerAstro and described here are filtering,3-D selection and model aided exploration of H I data. The system is still under de-velopment and we expect to add more features in the future. User feedback is greatlyappreciated and can be directed to the first two authors of this paper or be posted onhttps://github.com/Punzo/SlicerAstro.3. AcknowledgmentsD. Punzo and J.M van der Hulst gratefully acknowledge support from the European Re-search Council under the European Union’s Seventh Framework Programme (FP/2007-2013)/ERC Grant Agreement nr. 291531.ReferencesCalabretta, M. R. & Greisen, E. W. 2002, A&A, 395, 1077Duffy, A. R., Meyer, M. J., Staveley-Smith, L., et al. 2012, MNRAS, 426, 3385Di Teodoro, E. M. & Fraternali, F. 2015, MNRAS 451, 3021Gooch, R. 1996, Astronomical Data Analysis Software and Systems V, 101, 80Greisen, E. W. & Calabretta, M. R. 2002, A&A, 395, 1061Greisen, E. W., Calabretta, M. R., Valdes, F. G., & Allen, S. L. 2006, A&A, 446, 747Hassan, A. & Fluke, C. J. 2011, Publications of the Astronomical Society of Australia, 28, 150Johnston, S., Taylor, R., Bailes, M., et al. 2008, Experimental Astronomy, 22, 151Koribalski, B. S. 2012, Publications of the Astronomical Society of Australia, 29, 359Lorensen, W. E. & Cline, H. E. 1987, SIGGRAPH Comput. Graph. vol. 21, nr. 4, 163Norris, R. P. 1994, Astronomical Data Analysis Software and Systems III, 61, 51Oosterloo, T., Verheijen, M., & van Cappellen, W. 2010, ISKAF2010 Science Meeting, 43Perona, P & Malik, J. 1990, IEEE, 12, 629Punzo, D., van der Hulst, J. M., & Roerdink, J. B. T. M. 2016, Astronomy and Computing, 17,163Punzo, D., van der Hulst, J. M., Roerdink, J. B. T. M., et al. 2015, Astronomy and Computing,12, 86Sancisi, R., Fraternali, F., Oosterloo, T., & van der Hulst, T. 2008, A&A Review, 15, 189Serra, P., Westmeier, T., Giese, N., et al. 2015, MNRAS, 448, 1922Verheijen, M., Oosterloo, T., Heald, G., & van Cappellen, W. 2009, Panoramic Radio Astronomy:Wide-field 1-2 GHz Research on Galaxy Evolution, 10Wells, D. C., Greisen, E. W., & Harten, R. H. 1981, A&A Suppl., 44, 363Whiting, M. T. 2012, MNRAS, 421, 3242Wyvill, G., McPheeters, C., & Wyvill, B. 1986, The Visual Computer, vol. 2, nr. 4, 227Yu, L., Efstatiou, K., Isenberg, P., & Isenberg, T. 2012, IEEE Transactions on Visualizationand Computer Graphics, vol. 18, nr. 12, 2245Yu, L., Efstatiou, K., Isenberg, P., & Isenberg, T. 2012, IEEE Transactions on Visualizationand Computer Graphics, vol. 22, nr. 1, 1077https://www.cambridge.org/core/terms. https://doi.org/10.1017/S174392131700134XDownloaded from https://www.cambridge.org/core. University of Groningen, on 18 Jan 2018 at 08:30:54, subject to the Cambridge Core terms of use, available at

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3-D interactive visualisation tools for HI spectral line imaging

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