Characterizing Width Uniformity by Wave Propagation

This work describes a novel image analysis approach to characterize the uniformity of objects in agglomerates by using the propagation of normal wavefronts. The problem of width uniformity is discussed and its importance for the characterization of composite structures normally found in physics and biology highlighted. The methodology involves identifying each cluster (i.e. connected component) of interest, which can correspond to objects or voids, and estimating the respective medial axes by using a recently proposed wavefront propagation approach, which is briefly reviewed. The distance values along such axes are identified and their mean and standard deviation values obtained. As illustrated with respect to synthetic and real objects (in vitro cultures of neuronal cells), the combined use of these two features provide a powerful description of the uniformity of the separation between the objects, presenting potential for several applications in material sciences and biology.Comment: 14 pages, 23 figures, 1 table, 1 referenc

Neuromorphometric characterization with shape functionals

This work presents a procedure to extract morphological information from neuronal cells based on the variation of shape functionals as the cell geometry undergoes a dilation through a wide interval of spatial scales. The targeted shapes are alpha and beta cat retinal ganglion cells, which are characterized by different ranges of dendritic field diameter. Image functionals are expected to act as descriptors of the shape, gathering relevant geometric and topological features of the complex cell form. We present a comparative study of classification performance of additive shape descriptors, namely, Minkowski functionals, and the nonadditive multiscale fractal. We found that the proposed measures perform efficiently the task of identifying the two main classes alpha and beta based solely on scale invariant information, while also providing intraclass morphological assessment

Fast And Automatic Curvilinear Reformatting Of Mr Images Of The Brain For Diagnosis Of Dysplastic Lesions

Curvilinear reformatting is known as the best non-invasive technique for diagnosis of dysplastic lesions of the brain. It consists of computing surfaces that follow the brain's curvature at various depths, making the diagnosis possible by visual inspection of the voxel intensities on these surfaces. Traditional approaches require user intervention and present curvature artifacts. We present a new method for curvilinear reformatting that solves both problems. It uses a graph-based approach to segment the brain, extract its envelope, and compute the isosurfaces at all possible depths by euclidean distance transform. It requires no user input, no ad-hoc parameters, and takes less than 1 minute to run on a common PC. © 2006 IEEE.2006486489Barkovich, A.J., Rowley, H.A., Andermann, F., MR in partial epilepsy: Value of high-resolution volumetric techniques (1995) American Journal of Neuroradiology, 16, pp. 339-343. , FebBastos, A.C., Comeau, R.M., Andermann, F., Melanson, D., Cendes, F., Dubeau, F., Fontaine, S., Olivier, A., Diagnosis of subtle focal dysplastic lesions: Curvilinear reformatting from three-dimensional magnetic resonance imaging (1999) Annals of Neurology, 46 (1), pp. 88-94Colombo, N., Tassi, L., Galli, C., Citterio, A., Lo Russo, G., Scialfa, G., Spreafico, R., Focal cortical dysplasias: MR imaging, histopathologic, and clinical correlations in surgically treated patients with epilepsy (2003) American Journal of Neuroradiology, 24, pp. 724-733. , AprBrainSight, , http://www.rogue-research.com/B/epilepsy.htmFrackowiak, R.S.J., Friston, K.J., Frith, C., Dolan, R., Price, C.J., Zeki, S., Ashburner, J., Penny, W.D., (2003) Human Brain Function, , Academic Press, 2nd editionBueno, G., Musse, O., Heitz, F., Armspach, J.P., Three-dimensional segmentation of anatomical structures in MR images on large data bases (2001) Magnetic Resonance Imaging, 19, pp. 73-88Dougherty, E.R., Lotufo, R.A., (2003) Hands-on Morphological Image Processing, , SPIE Press, Bellingham, WAFalcão, A.X., Stolfi, J., Lotufo, R.A., The image foresting transform: Theory, algorithms, and applications (2004) IEEE Trans. on Pattern Analysis and Machine Intelligence, 26 (1), pp. 19-29Falcão, A.X., Bergo, F.P.G., Miranda, P.A.V., Image segmentation by tree pruning (2004) Proc. of the XVII Brazillian Symposium on Computer Graphics and Image Processing., pp. 65-71. , Oct , IEEEFalcão, A.X., Miranda, P.A.V., Bergo, F.P.G., (2005) Automatic Object Detection by Tree Pruning, , Tech. Rep. IC-05-19, Institute of Computing, University of Campinas, SepOtsu, N., A threshold selection method from gray level histograms (1979) IEEE Trans. Systems, Man and Cybernetics, 9, pp. 62-66. , Ma

Proceedings Of Sibgrapi 2007 - 20th Brazilian Symposium On Computer Graphics And Image Processing: Preface

[No abstract available

Tsd: A Shape Descriptor Based On A Distribution Of Tensor Scale Local Orientation

We present tensor scale descriptor (TSD) - a shape descriptor for content-based image retrieval, registration, and analysis. TSD exploits the notion of local structure thickness, orientation, and anisotropy as represented by the largest ellipse centered at each image pixel and within the same homogeneous region. The proposed method uses the normalized histogram of the local orientation (the angle of the ellipse) at regions of high anisotropy and thickness within a certain interval It is shown that TSD is invariant to rotation and to some reasonable level of scale changes. Experimental results with a fish database are presented to illustrate and validate the method. © 2005 IEEE.2005139146Abbasi, S., Mokhtarian, F., Kittler, J., Enhancing CSS-based Shape Retrieval for Objects with Shallow Concavities (2000) Image and Vision Computing, 18 (3), pp. 199-211. , FebruaryArica, N., Vural, F.T.Y., A Perceptual Shape Descriptor (2002) Proceedings of the International Conference on Pattern Recognition, pp. 375-378. , Madison, Wisconsin, USAArica, N., Vural, F.T.Y., BAS: A Perceptual Shape Descriptor Based on the Beam Angle Statistics (2003) Pattern Recognition Letters, pp. 1627-1639. , 249-10, JuneBelongie, S., Malik, J., Puzicha, J., Shape Matching and Object Recognition Using Shape Contexts (2002) IEEE Transactions on Pattern Analysis and Machine Intelligence, 24 (24), pp. 509-522Canny, J., A computational approach to edge detection (1986) IEEE Transactions on Pattern Analysis and Machine Intelligence, 8 (6), pp. 679-698Chuang, G., Kuo, C.-C., Wavelet Descriptor of Planar Curves: Theory and Applications (1996) IEEE Transactions on Pattern Analysis and Machine Intelligence, 5 (1), pp. 56-70Torres, R.D.S., Falcão, A.X., Costa, L.D.F., A Graph-based Approach for Multiscale Shape Analysis (2004) Pattern Recognition, 37 (6), pp. 1163-1174. , JuneTorres, R.D.S., Picado, E.M., Falcão, A.X., Costa, L.D.F., Effective Image Retrieval by Shape Saliences (2003) Proceedings of the Brazilian Symposium on Computer Graphics and Image Processing, pp. 49-55. , São Carlos, SP, Brazil, Octoberdi Zeno, S., A note on the gradient of a multi-image (1986) Computer Vision Graphics and Image Processing, 33 (1), pp. 166-125Dudani, S.A., Breeding, K.J., McGhee, R.B., Aircraft Identification by Moment Invariants (1977) IEEE Transactions on Computers, 26 (1), pp. 39-45. , JanuaryElder, J.H., Zucker, S.W., Local scale control for edge detection and blur estimation (1998) IEEE Transactions on Pattern Analysis and Machine Intelligence, 20 (7), pp. 699-716Gonzalez, R.C., Woods, R.E., (1992) Digital Image Processing, , Addison-Wesley, Reading, MA, USAHall, E.L., (1979) Computer Image Processing and Recognition, , New York, NY: Academic PressM. K. Hu. Visual Pattern Recognition by Moment Invariants. IRE Transactions on Information Theory, 8(2):179-187, 1962Kass, M., Witkin, A., Analyzing oriented patterns (1987) Computer Vision Graphics and Image Processing, 37 (3), pp. 362-385Khotanzan, A., Hong, Y.H., Invariant Image Recognition by Zernike Moments (1990) IEEE Transactions on Pattern Analysis and Machine Intelligence, 12 (5), pp. 489-487Kim, H., Kim, J., Region-based shape descriptor invariant to rotation, scale and translation (2000) Signal Process. Image Commun, 16 (1-2), pp. 87-93Knutsson, H., Representing local structures using tensors (1989) Proceedings of 6th Scandinavian Conference on Image Analysis, pp. 244-251Latecki, L.J., Lakamper, R., Shape Similarity Measure Based on Correspondence of Visual Parts (2000) IEEE Transactions on Pattern Analysis and Machine Intelligence, 22 (10), pp. 1185-1190Lin, L.-J., Kung, S.-Y., Coding and Comparison of DAGs as a Novel Neural Structure with Applications to On-Line Handwriting Recognition (1997) IEEE Transactions on Signal Processing, 45 (11), pp. 2701-2708Marr, D., Hildretch, E., Theory of edge detection (1980) Proceedings of Royal Society London, 207, pp. 187-217Mehtre, B.M., Kankanhalli, M.S., Lee, W.F., Shape Measures for Content Based Image Retrieval: A Comparison (1997) Information Processing and Management, 33 (3), pp. 319-337Ming, C.Y., (1999) Shape-Based Image Retrieval in Iconic Image Databases, , Master's thesis, Chinese University of Hong Kong, JuneMokhtarian, F., Abbasi, S., Shape Similarity Retrieval Under Affine Transforms (2002) Pattern Recognition, 35 (1), pp. 31-41. , JanuaryRieger, B., van Vliet, L.J., Curvature of n-dimensional space curve in grey-value images (2002) IEEE Transactions on Image Processing, 11 (7), pp. 738-745Saha, P., Tensor Scale: A Local Morphometric Parameter With Applications to Computer Vision and Image Processing (2003), Technical Report 306, Medical Image Processing Group, Department of Radiology, University of Pennsylvania, SeptemberSaha, P.K., Gee, J.C., Xie, Z., Udupa, J.K., Tensor scale-based image registration, in (2003) Proceedings of SPIE: Medical Imaging, 5032, pp. 743-753Saha, P.K., Udupa, J.K., Scale-based diffusive image filtering preserving boundary sharpness and fine structures (2001) IEEE Transactions on Medical Imaging, 20 (11), pp. 1140-1155Saha, P.K., Udupa, J.K., Tensor scale-based fuzzy connectedness image segmentation, in (2003) Proceedings of SPIE: Medical Imaging, 5032, pp. 1580-1590Saha, P.K., Udupa, J.K., Odhner, D., Scale-based fuzzy connected image segmentation: Theory, algorithms, and validation (2000) Computer Vision and Image Understanding, 77 (2), pp. 145-174(2005), www.ee.surrey.ac.uk/research/vssp/imagedb/demo.html, ShapeDB, MayTagare, H.D., deFigueiredo, R.J.P., On the localization performance measure and optimal edge detection (1990) IEEE Transactions on Pattern Analysis and Machine Intelligence, 12 (12), pp. 1186-1190van de Weijer, J., van Vliet, L.J., Verbeek, P.W., van Ginkel, M., Curvature estimation in oriented patters using curvilinear models applied to gradient vector fields (2002) IEEE Transactions on Pattern Analysis and Machine Intelligence, 23, pp. 1035-1043Zhang, D., Lu, G., Review of shape representation and description techniques (2004) Pattern Recognition, 37 (1), pp. 1-1

The Riverbed Approach For User-steered Image Segmentation

This work presents an optimum user-steered boundary tracking approach for image segmentation, which simulates the behavior of water flowing through a riverbed. The riverbed approach was devised using the Image Foresting Transform with a never exploited connectivity function. We analyze its properties in the derived image graphs and discuss its theoretical relation with other popular methods, such as live wire and graph cuts. Riverbed can significantly reduce the number of user interactions (anchor points) as compared to live wire for objects with complex shapes. © 2011 IEEE.31333136 IEEE,IEEE Signal Processing SocietyNilsson, N.J., (1980) Principles of Artificial Intelligence, , Morgan Kaufmann Publishers, San Francisco, CAFalcão, A.X., Udupa, J.K., Samarasekera, S., Sharma, S., Hirsch, B.E., Lotufo, R.A., User-steered image segmentation paradigms: Live-wire and live-lane (1998) Graphical Models and Image Processing, 60 (4), pp. 233-260. , JulMortensen, E.N., Barrett, W.A., Interactive segmentation with intelligent scissors (1998) Graphical Models and Image Processing, 60, pp. 349-384Falcão, A.X., Udupa, J.K., Miyazawa, F.K., An ultrafast user-steered image segmentation paradigm: Live-wire-on-the-fly (2000) IEEE Transactions on Medical Imaging, 19 (1), pp. 55-62. , JanMalmberg, F., Vidholm, E., Nystrom, I., A 3D live-wire segmentationmethod for volume images using haptic interaction (2006) Discrete Geometry for Computer Imagery, 4245, pp. 663-673Farber, M., Ehrhardt, J., Handels, H., Live-wire-based segmentation using similarities between corresponding image structures (2007) Computerized Medical Imaging and Graphics, 31 (7), pp. 549-560. , DOI 10.1016/j.compmedimag.2007.06.005, PII S0895611107000845Liu, J., Udupa, J.K., Oriented active shape models (2009) IEEE Transactions on Medical Imaging, 28 (4), pp. 571-584Grady, L., Random walks for image segmentation (2006) IEEE TPAMI, 28 (11), pp. 1768-1783Falcão, A.X., Stolfi, J., Lotufo, R.A., The image foresting transform: Theory, algorithms, and applications (2004) IEEE TPAMI, 26 (1), pp. 19-29Boykov, Y., Funka-Lea, G., Graph cuts and efficient N-D image segmentation (2006) International Journal of Computer Vision, 70 (2), pp. 109-131. , DOI 10.1007/s11263-006-7934-5Audigier, R., Lotufo, R.A., Seed-relative segmentation robustness of watershed and fuzzy connectedness approaches Proc. of the Brazilian Symp. on Computer Graphics and Image Processing, 2007, pp. 61-68Miranda, P.A.V., Falcão, A.X., Links between image segmentation based on optimum-path forest and minimum cut in graph (2009) Journal of Mathematical Imaging and Vision, 35 (2), pp. 128-142Miranda, P.A.V., Falcão, A.X., Udupa, J.K., Synergistic arc-weight estimation for interactive image segmentation using graphs (2010) Computer Vision and Image Understanding, 114 (1), pp. 85-99. , JanMiranda, P.A.V., Falcão, A.X., Spina, T.V., (2011) Riverbed: A Novel User-steered Image Segmentation Method Based on Optimum Boundary Tracking, , www.ic.unicamp.br/~reltech/2011/abstracts.html, Tech. Rep. IC-11-04, Institute of Computing, JanRother, C., Kolmogorov, V., Blake, A., "grabcut": Interactive foreground extraction using iterated graph cuts (2004) ACM Transactions on Graphics, 23 (3), pp. 309-31

A Fast And Automatic Method For 3d Rigid Registration Of Mr Images Of The Human Brain

Image registration is an important problem with several applications in Medical Imaging. Intra-subject rigid registration requires a minimal set of parameters to be computed, and is sufficient for organs with no significant movement or deformation, such as the human brain. Rigid registration has also been used as the first step before inter-subject deformable registration. In this paper we present a fast and automatic method for 3D rigid registration of magnetic resonance images of the human brain. The method combines previous approaches for mid-sagittal plane location and brain segmentation with a greedy-search algorithm to find the best match between source and target images. We evaluated the method on 200 image pairs: 100 without structural abnormalities and 100 with artificially created lesions, such that it was possible to quantify the registration errors. The method achieved very accurate registration within a few seconds. © 2008 IEEE.121128Audette, M., Ferrie, F., Peters, T., An algorithmic overview of surface registration techniques for medical imaging (2000) Medical Image Analysis, 4 (3), pp. 201-217Bergo, F.P.G., Falcão, A.X., Miranda, P.A.V., Rocha, L.M., Automatic image segmentation by tree pruning (2007) J Math Imaging and Vision, 29 (2-3), pp. 141-162. , NovF. P. G. Bergo, G. C. S. Ruppert, L. F. Pinto, and A. X. Falcão. Fast and robust mid-sagittal plane location in 3D MR images of the brain. In Proc. BIOSIGNALS 2008 - Intl. Conf. on Bio-Inspired Syst. and Sig. Proc., pages 92-99, Jan 2008Besl, P.J., McKay, N.D., A method for registration of 3-d shapes (1992) IEEE Transactions on pattern analysis and machine intelligence, 14 (2), pp. 239-256Brown, L.G., A survey of image registration techniques (1992) ACM. 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L. Yasuda, C. C. Valise, A. V. Saúde, A. L. F. Costa, F. R. Pereira, M. Morita, L. E. Betting, H. Tedeschi, E. Oliveira, G. Castellano, and F. Cendes. Recovery of white matter atrophy (WMA) after successful surgery in mesial TLE. In Proc. 60th Annual Meeting of the American Academy of Neurology, page to appear, 2008Zhou, H., Liu, T., Lin, F., Pang, Y., Wu, J., Wu, J., Towards efficient registration of medical images (2007) Computerized Medical Imaging and Graphics, 31, pp. 374-382. , SepZivota, B., Flusser, J., Image registration methods: A survey (2003) Image and Vision Computing, 21, pp. 977-1000Zou, X., Zhao, X., Feng, Y., An efficient medical image registration algorithm based on gradient descent (2007) Complex Medical Engineering, pp. 636-639. , Ma

Shear-Warp Shell Rendering

In Medical Imaging, shell rendering (SR) and shear-warp rendering (SWR) are two ultrafast and eective methods for volume visualization. We have previously shown the fact that, typically, SWR can be on the average 1.38 times faster than SR, but it requires from 2 to 8 times more memory space than SR. In this paper, we propose an extension of the compact shell data structure utilized in SR to allow shear-warp factorization of the viewing matrix in order to obtain speed up gains for SR, without paying the high storage price of SWR. The new approach is called shear-warp shell rendering (SWSR). The paper describes the methods, points out their major dierences in the computational aspects, and presents a comparative analysis of them in terms of speed, storage, and image quality. The experiments involve hard and fuzzy boundaries of 10 dierent objects of various sizes, shapes, and topologies, rendered on a 1GHz Pentium-III PC with 512MB RAM, utilizing surface and volume rendering strategies. The results indicate that SWSR oers the best speed and storage characteristics compromise among these methods