Norman Zerbe

Peter Hufnagl

Karsten Schlüns

K Saeger

R Zwönitzer

MD Abramoff

J Schindelin

TJ Collins

Y Sun

W Huang

K Kayser

T Yeo

M Subbarao

Diagnostic Pathology

English

PubMed

Crossref

PROCEEDINGS Open AccessDistributed computing in image analysis usingopen source frameworks and application toimage sharpness assessment of histologicalwhole slide imagesNorman Zerbe1,2*, Peter Hufnagl1,2, Karsten Schlüns1From The 10th European Congress on Telepathology and 4th International Congress on Virtual MicroscopyVilnius, Lithuania. 1-3 July 2010AbstractBackground: Automated image analysis on virtual slides is evolving rapidly and will play an important role in thefuture of digital pathology. Due to the image size, the computational cost of processing whole slide images (WSIs)in full resolution is immense. Moreover, image analysis requires well focused images in high magnification.Methods: We present a system that merges virtual microscopy techniques, open source image analysis software,and distributed parallel processing. We have integrated the parallel processing framework JPPF, so batchprocessing can be performed distributed and in parallel. All resulting meta data and image data are collected andmerged. As an example the system is applied to the specific task of image sharpness assessment. ImageJ is anopen source image editing and processing framework developed at the NIH having a large user community thatcontributes image processing algorithms wrapped as plug-ins in a wide field of life science applications. Wedeveloped an ImageJ plug-in that supports both basic interactive virtual microscope and batch processingfunctionality. For the application of sharpness inspection we employ an approach with non-overlapping tiles.Compute nodes retrieve image tiles of moderate size from the streaming server and compute the focus measure.Each tile is divided into small sub images to calculate an edge based sharpness criterion which is used forclassification. The results are aggregated in a sharpness map.Results: Based on the system we calculate a sharpness measure and classify virtual slides into one of the followingcategories - excellent, okay, review and defective. Generating a scaled sharpness map enables the user to evaluatesharpness of WSIs and shows overall quality at a glance thus reducing tedious assessment work.Conclusions: Using sharpness assessment as an example, the introduced system can be used to process, analyzeand parallelize analysis of whole slide images based on open source software.BackgroundAutomated analysis of whole slide images (WSIs) is agrowing challenge in scientific research in digital pathol-ogy. Image processing software usually works on stan-dard-size images whereas virtual slides are very large.Due to the image size the computational cost ofprocessing whole slide images in high resolution isimmense. We present a system that combines virtualmicroscopy and public domain image analysis softwarerunning within a distributed parallel processing frame-work available in open source.In terms of image analysis slide preparation, illumina-tion, focus, and stitching are crucial factors for highquality scans. Image acquisition process for whole slideimages has been considerably improved over the lastyears. Nevertheless, manual inspection of image* Correspondence: norman.zerbe@charite.de1Institute of Pathology, Charité – Universitätsmedizin Berlin, Charitéplatz 1,10117, Berlin, GermanyFull list of author information is available at the end of the articleZerbe et al. Diagnostic Pathology 2011, 6(Suppl 1):S16http://www.diagnosticpathology.org/content/6/S1/S16© 2011 Zerbe et al; licensee BioMed Central Ltd. This is an open access article distributed under the terms of the Creative CommonsAttribution License (http://creativecommons.org/licenses/by/2.0), which permits unrestricted use, distribution, and reproduction inany medium, provided the original work is properly cited.sharpness is often still required after scanning. It is atedious and time consuming task with a high inter andintra observer variability. We choose automatic sharp-ness assessment on histological WSIs as an exemplaryapplication of distributed image analysis. It demonstrateshow the new system can be used for standardized imagequality assessment in high throughput scenarios.MethodsWith the availability of public domain image processinglibraries and free open source parallelization frame-works, we have combined these with recent virtualmicroscopy technologies such as WSI streaming servers[1,2] to provide a free processing environment for rapidprototyping of image analysis algorithms for WSIs.NIH ImageJ [3,4] is an interactive open source imageprocessing software and library developed at theNational Institutes of Health. Its large user communitycontributes extensions (plug-ins) in a wide field of lifescience applications. Several plug-in bundles for micro-scopic images are available that are based on ImageJ[5,6]. We have developed a plug-in to add capabilitiesfor handling WSIs.Algorithms with large processing power requirements,such as most image analysis tasks on WSIs, can often besplit into smaller parts and executed simultaneously ondifferent computers. In this work the Java Parallel Pro-cessing Framework (JPPF) was used to process WSIs onstandard PCs as compute nodes [7]. We employed anapproach with non-overlapping tiles in a predefinedresolution, where compute nodes retrieve image tiles ofmoderate size from a WSI streaming server. Each nodeprocesses the same set of ImageJ plug-ins independently.All resulting data are collected and merged within theJPPF environment using domain specific mergerimplementations.Many different criteria exist to measure sharpness inan image [8-12]. Sun et al. compared 18 focus algo-rithms where sum-modified-Laplacian (SML) showedthe best overall results [8]. Wei et al. confirmed its qual-ity [9]. Besides, the well known Tenenbaum gradient hasbeen found to be a reasonable focus measure whenexecution time is taken into account. Most publicationsdeal with autofocus applications, where the sharpestimage in a stack of focus images has to be found duringimage acquisition. In non z-stack WSIs sharpness has tobe evaluated on a single focus plane after slide scanning.Analysis has to be done in full resolution, since subsampling of images results in sharper images. We used amodified Tenenbaum gradient for sharpness assessmentapplication and applied it to non-overlapping tiles.Compute nodes simultaneously determine the focusmeasure for distinct image tiles in full resolution. Eachtile is divided into small sub images to calculate thesharpness measure. The results are aggregated in asharpness map.ResultsWe upgraded ImageJ to a virtual microscope (Fig. 1). Sev-eral WSI formats were added to ImageJ includingJPEG2000 (ISO), NDPI (Hamamatsu), Zoomify (e.g. Meta-systems, Leica), and Mirax (3DHISTECH). File systeminterfaces as well as web based WSI streaming servers areprovided as image data sources to access virtual slides.Based on the ImageJ framework we developed a plug-inthat shows a graphical user interface like a standard virtualmicroscope. Navigation elements such as panning andcontinuous zooming are offered. In addition to this, themicroscope allows doing batch processing of WSIs.We developed a seamless integration of the ImageJplug-in interface and WSI streaming technologies withinJPPF (Fig. 2). This enables the user to rapidly developand test algorithms inside the ImageJ microscope anduse this without any modifications inside a distributedand parallelized environment.The scalable system was employed to refine and paral-lelize a gradient based sharpness measure. Its resultswere used to generate a sharpness map for subsequentvisual inspection (Fig. 3). The map allows a user to eval-uate overall focus quality at a glance. Virtual slides areclassified into four sharpness categories – excellentquality, acceptable quality, to be reviewed and defectivequality.Figure 4 shows the overall amount of processing timeneeded to inspect sharpness as a function of two vari-ables, tissue area and number of compute nodes. Tissuearea to inspect is given in gigapixels. Time is measuredin hours, minutes and seconds h:mm:ss.DiscussionAutomatic sharpness assessment of whole slide imagesis an exemplary application of the proposed framework.It could be shown that the chosen sharpness measurereproduces results of manual sharpness inspection.JPPF offers functions to integrate standard PCs into aparallelization grid by running a worker node servicewith low priority. Standard office PCs can be employedfor parallel processing purposes without interfering withregular work. Moreover, no additional costs for infra-structure or maintenance are generated.So far limiting factors for scalability of the introducedsystem are 1) speed of delivering source images by theimage provider and 2) amount of result data to bemerged after processing. To improve image providerspeed we are currently adding load balancing capabilitiesfor multiple WSI streaming servers. Merging result datainside a relational database management system canresolve limitations of memory based merging.Zerbe et al. Diagnostic Pathology 2011, 6(Suppl 1):S16http://www.diagnosticpathology.org/content/6/S1/S16Page 2 of 5Figure 1 ImageJ virtual microscope. ImageJ was upgraded to a virtual microscope by connecting a multi format WSI streaming server as animage data provider.Figure 2 System overview. A) The ImageJ microscope can be used for rapid prototyping purposes. B) An image processing job is divided intoseveral tasks. Image tiles are processed on a grid of standard PCs (compute nodes). Each node receives image tiles and processes predefinedplug-ins. Results are merged using a domain specific implementation. C) Application to sharpness assessment.Zerbe et al. Diagnostic Pathology 2011, 6(Suppl 1):S16http://www.diagnosticpathology.org/content/6/S1/S16Page 3 of 5Figure 3 Sharpness map. Output of sharpness assessment procedure. Each pixel represents the result of a sub tile with a dimension of128x128 pixels (green – sharp tile, red – blurred tile, light green – sharp tile with high percentage of background, light red – blurred tile withhigh percentage of background, yellow – medium sharp tile, gray – background).Figure 4 Processing time. Line chart of total processing time versus number of compute nodes and tissue area given in gigapixels. Time ismeasured in hours, minutes and seconds h:mm:ss.Zerbe et al. Diagnostic Pathology 2011, 6(Suppl 1):S16http://www.diagnosticpathology.org/content/6/S1/S16Page 4 of 5ConclusionsThe most prominent toolkit in open source medicalimage analysis, ImageJ with its wide range of availablealgorithms, has been made applicable to virtual micro-scopy. Hence, based on the presented components it ispossible to interactively as well as automatically processand analyze WSIs within ImageJ. Furthermore, distribu-ted batch processing of WSIs using an open source par-allel processing framework (JPPF) is seamlesslyintegrated with the plug-in concept of ImageJ.The introduced open system architecture can beadapted within various application contexts and provesflexibility and scalability. Furthermore, it reduces proces-sing time. The introduced bridging technology betweenImageJ, JPPF, and virtual microscopy image streamingservers has applications in rapid prototyping of scientificimage analysis software, education and is also an optionfor cost effective solutions in upcoming high throughputimage analysis scenarios.AcknowledgementsThis work was supported by the German Federal State of Berlin in theframework of the “Zukunftsfonds Berlin” and the Technology FoundationInnovation center Berlin (TSB) within the project “Virtual Specimen Scout”. Itwas co-financed by the European Union within the European RegionalDevelopment Fund (EFRE).This article has been published as part of Diagnostic Pathology Volume 6Supplement 1, 2011: Proceedings of the 10th European Congress onTelepathology and 4th International Congress on Virtual Microscopy. The fullcontents of the supplement are available online at http://www.diagnosticpathology.org/supplements/6/S1Author details1Institute of Pathology, Charité – Universitätsmedizin Berlin, Charitéplatz 1,10117, Berlin, Germany. 2University of Applied Sciences Berlin,Wilhelminenhofstraße 75A, 12459, Berlin, Germany.Competing interestsThe authors declare that they have no competing interests.Published: 30 March 2011References1. Saeger K, Schlüns K, Schrader T, Hufnagl P: The virtual microscope forroutine pathology based on a PACS system for 6 Gb images. InProceedings of the 17th International Congress and Exhibition CARS 2003: 25-28 June 2003; London. Amsterdam: Elsevier;Lemke HU 2003:299-304.2. Zwönitzer R, Kalinski T, Hofmann H, Roessner A, Bernarding J: Digitalpathology: DICOM-conform draft, testbed, and first results. ComputMethods Programs Biomed 2007, 87:181-188.3. NIH ImageJ:[http://rsb.info.nih.gov/ij].4. Abramoff MD, Magelhaes PJ, Ram SJ: Image Processing with ImageJ.Biophotonics International 2004, 11:36-42.5. Schindelin J: Fiji is just ImageJ - Batteries included. Proceedings of theImageJ User and Developer Conference: 6-7 November 2008 Luxembourg;2008.6. Collins TJ: ImageJ for microscopy. Biotechniques 2007, 43:25-30.7. Java Parallel Processing Framework:[http://www.jppf.org].8. Sun Y, Duthaler S, Nelson BJ: Autofocusing in computer microscopy:selecting the optimal focus algorithm. Microsc Res Tech 2004, 65:139-149.9. Huang W, Jing Z: Evaluation of focus measures in multi-focus imagefusion. Pattern Recognit Lett 2007, 28:493-500.10. Kayser K, Görtler J, Metze K, Goldmann T, Vollmer E, Mireskandari M,Kosjerina Z, Kayser G: How to measure image quality in tissue-baseddiagnosis (diagnostic surgical pathology). Diagn Pathol 2008, 3(Suppl1):11.11. Yeo T, Ong S, Jayasooriah SR: Autofocusing for tissue microscopy. ImageVis Comput 1993, 11:629-639.12. Subbarao M, Choi T, Nikzad A: Focusing Techniques. In Machine VisionApplications Architectures, and Systems Integration (Proceedings of SPIE): 17-18November 1992; Boston. SPIE;Batchelor BG 1992:163-174.doi:10.1186/1746-1596-6-S1-S16Cite this article as: Zerbe et al.: Distributed computing in image analysisusing open source frameworks and application to image sharpnessassessment of histological whole slide images. Diagnostic Pathology 20116(Suppl 1):S16.Submit your next manuscript to BioMed Centraland take full advantage of: • Convenient online submission• Thorough peer review• No space constraints or color figure charges• Immediate publication on acceptance• Inclusion in PubMed, CAS, Scopus and Google Scholar• Research which is freely available for redistributionSubmit your manuscript at www.biomedcentral.com/submitZerbe et al. Diagnostic Pathology 2011, 6(Suppl 1):S16http://www.diagnosticpathology.org/content/6/S1/S16Page 5 of 5

Distributed computing in image analysis using open source frameworks and application to image sharpness assessment of histological whole slide images

P. W. M. Peters

A. Lystrup

S.-I. Andersen

A Novel Tool to Determine the Optimum Fabrication Parameters of Thermoplastic CFRP Laminates

Peters, P.W.M.

Lystrup, A.

Andersen, S.I.

Institute of Transport Research:Publications

A Novel Tool to Determine the Optimum Fabrication Parameters of Thermoplastic CFRP Laminates.

Springer - Publisher Connector

PROCEEDINGS Open Access
Distributed computing in image analysis using
open source frameworks and application to
image sharpness assessment of histological
whole slide images
Norman Zerbe1,2*, Peter Hufnagl1,2, Karsten Schlüns1
From The 10th European Congress on Telepathology and 4th International Congress on Virtual Microscopy
Vilnius, Lithuania. 1-3 July 2010
Abstract
Background: Automated image analysis on virtual slides is evolving rapidly and will play an important role in the
future of digital pathology. Due to the image size, the computational cost of processing whole slide images (WSIs)
in full resolution is immense. Moreover, image analysis requires well focused images in high magnification.
Methods: We present a system that merges virtual microscopy techniques, open source image analysis software,
and distributed parallel processing. We have integrated the parallel processing framework JPPF, so batch
processing can be performed distributed and in parallel. All resulting meta data and image data are collected and
merged. As an example the system is applied to the specific task of image sharpness assessment. ImageJ is an
open source image editing and processing framework developed at the NIH having a large user community that
contributes image processing algorithms wrapped as plug-ins in a wide field of life science applications. We
developed an ImageJ plug-in that supports both basic interactive virtual microscope and batch processing
functionality. For the application of sharpness inspection we employ an approach with non-overlapping tiles.
Compute nodes retrieve image tiles of moderate size from the streaming server and compute the focus measure.
Each tile is divided into small sub images to calculate an edge based sharpness criterion which is used for
classification. The results are aggregated in a sharpness map.
Results: Based on the system we calculate a sharpness measure and classify virtual slides into one of the following
categories - excellent, okay, review and defective. Generating a scaled sharpness map enables the user to evaluate
sharpness of WSIs and shows overall quality at a glance thus reducing tedious assessment work.
Conclusions: Using sharpness assessment as an example, the introduced system can be used to process, analyze
and parallelize analysis of whole slide images based on open source software.
Background
Automated analysis of whole slide images (WSIs) is a
growing challenge in scientific research in digital pathol-
ogy. Image processing software usually works on stan-
dard-size images whereas virtual slides are very large.
Due to the image size the computational cost of
processing whole slide images in high resolution is
immense. We present a system that combines virtual
microscopy and public domain image analysis software
running within a distributed parallel processing frame-
work available in open source.
In terms of image analysis slide preparation, illumina-
tion, focus, and stitching are crucial factors for high
quality scans. Image acquisition process for whole slide
images has been considerably improved over the last
years. Nevertheless, manual inspection of image
* Correspondence: norman.zerbe@charite.de
1Institute of Pathology, Charité – Universitätsmedizin Berlin, Charitéplatz 1,
10117, Berlin, Germany
Full list of author information is available at the end of the article
Zerbe et al. Diagnostic Pathology 2011, 6(Suppl 1):S16
http://www.diagnosticpathology.org/content/6/S1/S16
© 2011 Zerbe et al; licensee BioMed Central Ltd. This is an open access article distributed under the terms of the Creative Commons
Attribution License (http://creativecommons.org/licenses/by/2.0), which permits unrestricted use, distribution, and reproduction in
any medium, provided the original work is properly cited.
sharpness is often still required after scanning. It is a
tedious and time consuming task with a high inter and
intra observer variability. We choose automatic sharp-
ness assessment on histological WSIs as an exemplary
application of distributed image analysis. It demonstrates
how the new system can be used for standardized image
quality assessment in high throughput scenarios.
Methods
With the availability of public domain image processing
libraries and free open source parallelization frame-
works, we have combined these with recent virtual
microscopy technologies such as WSI streaming servers
[1,2] to provide a free processing environment for rapid
prototyping of image analysis algorithms for WSIs.
NIH ImageJ [3,4] is an interactive open source image
processing software and library developed at the
National Institutes of Health. Its large user community
contributes extensions (plug-ins) in a wide field of life
science applications. Several plug-in bundles for micro-
scopic images are available that are based on ImageJ
[5,6]. We have developed a plug-in to add capabilities
for handling WSIs.
Algorithms with large processing power requirements,
such as most image analysis tasks on WSIs, can often be
split into smaller parts and executed simultaneously on
different computers. In this work the Java Parallel Pro-
cessing Framework (JPPF) was used to process WSIs on
standard PCs as compute nodes [7]. We employed an
approach with non-overlapping tiles in a predefined
resolution, where compute nodes retrieve image tiles of
moderate size from a WSI streaming server. Each node
processes the same set of ImageJ plug-ins independently.
All resulting data are collected and merged within the
JPPF environment using domain specific merger
implementations.
Many different criteria exist to measure sharpness in
an image [8-12]. Sun et al. compared 18 focus algo-
rithms where sum-modified-Laplacian (SML) showed
the best overall results [8]. Wei et al. confirmed its qual-
ity [9]. Besides, the well known Tenenbaum gradient has
been found to be a reasonable focus measure when
execution time is taken into account. Most publications
deal with autofocus applications, where the sharpest
image in a stack of focus images has to be found during
image acquisition. In non z-stack WSIs sharpness has to
be evaluated on a single focus plane after slide scanning.
Analysis has to be done in full resolution, since sub
sampling of images results in sharper images. We used a
modified Tenenbaum gradient for sharpness assessment
application and applied it to non-overlapping tiles.
Compute nodes simultaneously determine the focus
measure for distinct image tiles in full resolution. Each
tile is divided into small sub images to calculate the
sharpness measure. The results are aggregated in a
sharpness map.
Results
We upgraded ImageJ to a virtual microscope (Fig. 1). Sev-
eral WSI formats were added to ImageJ including
JPEG2000 (ISO), NDPI (Hamamatsu), Zoomify (e.g. Meta-
systems, Leica), and Mirax (3DHISTECH). File system
interfaces as well as web based WSI streaming servers are
provided as image data sources to access virtual slides.
Based on the ImageJ framework we developed a plug-in
that shows a graphical user interface like a standard virtual
microscope. Navigation elements such as panning and
continuous zooming are offered. In addition to this, the
microscope allows doing batch processing of WSIs.
We developed a seamless integration of the ImageJ
plug-in interface and WSI streaming technologies within
JPPF (Fig. 2). This enables the user to rapidly develop
and test algorithms inside the ImageJ microscope and
use this without any modifications inside a distributed
and parallelized environment.
The scalable system was employed to refine and paral-
lelize a gradient based sharpness measure. Its results
were used to generate a sharpness map for subsequent
visual inspection (Fig. 3). The map allows a user to eval-
uate overall focus quality at a glance. Virtual slides are
classified into four sharpness categories – excellent
quality, acceptable quality, to be reviewed and defective
quality.
Figure 4 shows the overall amount of processing time
needed to inspect sharpness as a function of two vari-
ables, tissue area and number of compute nodes. Tissue
area to inspect is given in gigapixels. Time is measured
in hours, minutes and seconds h:mm:ss.
Discussion
Automatic sharpness assessment of whole slide images
is an exemplary application of the proposed framework.
It could be shown that the chosen sharpness measure
reproduces results of manual sharpness inspection.
JPPF offers functions to integrate standard PCs into a
parallelization grid by running a worker node service
with low priority. Standard office PCs can be employed
for parallel processing purposes without interfering with
regular work. Moreover, no additional costs for infra-
structure or maintenance are generated.
So far limiting factors for scalability of the introduced
system are 1) speed of delivering source images by the
image provider and 2) amount of result data to be
merged after processing. To improve image provider
speed we are currently adding load balancing capabilities
for multiple WSI streaming servers. Merging result data
inside a relational database management system can
resolve limitations of memory based merging.
Zerbe et al. Diagnostic Pathology 2011, 6(Suppl 1):S16
http://www.diagnosticpathology.org/content/6/S1/S16
Page 2 of 5
Figure 1 ImageJ virtual microscope. ImageJ was upgraded to a virtual microscope by connecting a multi format WSI streaming server as an
image data provider.
Figure 2 System overview. A) The ImageJ microscope can be used for rapid prototyping purposes. B) An image processing job is divided into
several tasks. Image tiles are processed on a grid of standard PCs (compute nodes). Each node receives image tiles and processes predefined
plug-ins. Results are merged using a domain specific implementation. C) Application to sharpness assessment.
Zerbe et al. Diagnostic Pathology 2011, 6(Suppl 1):S16
http://www.diagnosticpathology.org/content/6/S1/S16
Page 3 of 5
Figure 3 Sharpness map. Output of sharpness assessment procedure. Each pixel represents the result of a sub tile with a dimension of
128x128 pixels (green – sharp tile, red – blurred tile, light green – sharp tile with high percentage of background, light red – blurred tile with
high percentage of background, yellow – medium sharp tile, gray – background).
Figure 4 Processing time. Line chart of total processing time versus number of compute nodes and tissue area given in gigapixels. Time is
measured in hours, minutes and seconds h:mm:ss.
Zerbe et al. Diagnostic Pathology 2011, 6(Suppl 1):S16
http://www.diagnosticpathology.org/content/6/S1/S16
Page 4 of 5
Conclusions
The most prominent toolkit in open source medical
image analysis, ImageJ with its wide range of available
algorithms, has been made applicable to virtual micro-
scopy. Hence, based on the presented components it is
possible to interactively as well as automatically process
and analyze WSIs within ImageJ. Furthermore, distribu-
ted batch processing of WSIs using an open source par-
allel processing framework (JPPF) is seamlessly
integrated with the plug-in concept of ImageJ.
The introduced open system architecture can be
adapted within various application contexts and proves
flexibility and scalability. Furthermore, it reduces proces-
sing time. The introduced bridging technology between
ImageJ, JPPF, and virtual microscopy image streaming
servers has applications in rapid prototyping of scientific
image analysis software, education and is also an option
for cost effective solutions in upcoming high throughput
image analysis scenarios.
Acknowledgements
This work was supported by the German Federal State of Berlin in the
framework of the “Zukunftsfonds Berlin” and the Technology Foundation
Innovation center Berlin (TSB) within the project “Virtual Specimen Scout”. It
was co-financed by the European Union within the European Regional
Development Fund (EFRE).
This article has been published as part of Diagnostic Pathology Volume 6
Supplement 1, 2011: Proceedings of the 10th European Congress on
Telepathology and 4th International Congress on Virtual Microscopy. The full
contents of the supplement are available online at http://www.
diagnosticpathology.org/supplements/6/S1
Author details
1Institute of Pathology, Charité – Universitätsmedizin Berlin, Charitéplatz 1,
10117, Berlin, Germany. 2University of Applied Sciences Berlin,
Wilhelminenhofstraße 75A, 12459, Berlin, Germany.
Competing interests
The authors declare that they have no competing interests.
Published: 30 March 2011
References
1. Saeger K, Schlüns K, Schrader T, Hufnagl P: The virtual microscope for
routine pathology based on a PACS system for 6 Gb images. In
Proceedings of the 17th International Congress and Exhibition CARS 2003: 25-
28 June 2003; London. Amsterdam: Elsevier;Lemke HU 2003:299-304.
2. Zwönitzer R, Kalinski T, Hofmann H, Roessner A, Bernarding J: Digital
pathology: DICOM-conform draft, testbed, and first results. Comput
Methods Programs Biomed 2007, 87:181-188.
3. NIH ImageJ:[http://rsb.info.nih.gov/ij].
4. Abramoff MD, Magelhaes PJ, Ram SJ: Image Processing with ImageJ.
Biophotonics International 2004, 11:36-42.
5. Schindelin J: Fiji is just ImageJ - Batteries included. Proceedings of the
ImageJ User and Developer Conference: 6-7 November 2008 Luxembourg;
2008.
6. Collins TJ: ImageJ for microscopy. Biotechniques 2007, 43:25-30.
7. Java Parallel Processing Framework:[http://www.jppf.org].
8. Sun Y, Duthaler S, Nelson BJ: Autofocusing in computer microscopy:
selecting the optimal focus algorithm. Microsc Res Tech 2004, 65:139-149.
9. Huang W, Jing Z: Evaluation of focus measures in multi-focus image
fusion. Pattern Recognit Lett 2007, 28:493-500.
10. Kayser K, Görtler J, Metze K, Goldmann T, Vollmer E, Mireskandari M,
Kosjerina Z, Kayser G: How to measure image quality in tissue-based
diagnosis (diagnostic surgical pathology). Diagn Pathol 2008, 3(Suppl
1):11.
11. Yeo T, Ong S, Jayasooriah SR: Autofocusing for tissue microscopy. Image
Vis Comput 1993, 11:629-639.
12. Subbarao M, Choi T, Nikzad A: Focusing Techniques. In Machine Vision
Applications Architectures, and Systems Integration (Proceedings of SPIE): 17-18
November 1992; Boston. SPIE;Batchelor BG 1992:163-174.
doi:10.1186/1746-1596-6-S1-S16
Cite this article as: Zerbe et al.: Distributed computing in image analysis
using open source frameworks and application to image sharpness
assessment of histological whole slide images. Diagnostic Pathology 2011
6(Suppl 1):S16.
Submit your next manuscript to BioMed Central
and take full advantage of: 
• Convenient online submission
• Thorough peer review
• No space constraints or color figure charges
• Immediate publication on acceptance
• Inclusion in PubMed, CAS, Scopus and Google Scholar
• Research which is freely available for redistribution
Submit your manuscript at 
www.biomedcentral.com/submit
Zerbe et al. Diagnostic Pathology 2011, 6(Suppl 1):S16
http://www.diagnosticpathology.org/content/6/S1/S16
Page 5 of 5


Lystrup, Aage

Andersen, Svend Ib Smidt

Online Research Database In Technology

A novel tool to determine the optimum fabrication parameters of thermoplastic CFRP laminates

BJ: Autofocusing in computer microscopy: selecting the optimal focus algorithm. Microsc Res Tech

Digital pathology: DICOM-conform draft, testbed, and first results. Comput Methods Programs Biomed

Fiji is just ImageJ - Batteries included.

Focusing Techniques.

How to measure image quality in tissue-based diagnosis (diagnostic surgical pathology). Diagn Pathol

ImageJ for microscopy. Biotechniques

Java Parallel Processing Framework:[http://www.jppf.org].

Jing Z: Evaluation of focus measures in multi-focus image fusion. Pattern Recognit Lett

SJ: Image Processing with ImageJ. Biophotonics International

SR: Autofocusing for tissue microscopy. Image Vis Comput

The virtual microscope for routine pathology based on a PACS system for 6 Gb images.

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Distributed computing in image analysis using open source frameworks and application to image sharpness assessment of histological whole slide images

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