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Supercomputers and client-server environment for biomedical image processing

Supercomputers and client-server environment for biomedical image processing. C. Dufour and J.-Ph. Thiran. In other words. How to take advantage of supercomputers processing power through client-server applications for biomedical image processing. EPFL’s Swiss-TX and Java applets.

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Supercomputers and client-server environment for biomedical image processing

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  1. Supercomputersandclient-server environment for biomedical image processing C. Dufour and J.-Ph. Thiran

  2. In other words... How to take advantage ofsupercomputers processing powerthroughclient-server applications for biomedical image processing EPFL’s Swiss-TX and Java applets

  3. Contents • Biomedical image processing facts • Supercomputers in a client-server environment. • EPFL’s supercomputer: the Swiss-TX • Javabased client-server applications • Fundamentals on image processing parallellization • A practical example: the Microtubules application • Conclusions

  4. Biomedical image processing facts Biomedical image processing is very demanding in computer power because of… • Image data size • Electron microscopy 512x512 images  2 Mb • X-Ray 2048x2048 images  32 Mb • MRI 256x256x100 images  50 Mb • Confocal microscopy 512x512x50x3 images  300 Mb • Processing algorithm complexity • Many processing operations required to reach the final solution • Some operations may be highly iterative • Some algorithm may be very complex

  5. Biomedical image processing facts Examples of such applications are… • 3D image registration • MRI-CT rigid registration • Brain atlas matching (non-rigid registration) • 2D image processing • Software tool for early diagnosis of malignant melanoma • 3D object based image compression • Heart image compression • 2D and 3D microscopy image analysis • The Microtubules image analysis application • Correlation analysis of pre- and post-synaptic proteins (from Confocal images)

  6. Biomedical image processing facts • Processing time for large images and/or complex algorithms may be particularly high. • Processing time may be reduced significantly through use of supercomputers and proper parallellization of processing algorithms.

  7. Image data PC Network Supercomputer AcquisitionViewing Processing Supercomputers in a client-server environment • Allow anyuser (biologist, physician) to take advantage of supercomputer(s) processing power through the network Client Server

  8. EPFL’s supercomputer: the Swiss-TX • The Swiss-TX is EPFL’s new supercomputer.Its main characteristics are :

  9. EPFL’s supercomputer: the Swiss-TX • The Swiss-TX is EPFL’s new supercomputer.Its main characteristics are : • Commodity based supercomputer

  10. EPFL’s supercomputer: the Swiss-TX • The Swiss-TX is EPFL’s new supercomputer.Its main characteristics are : • Commodity based supercomputer • Up to 504 processing nodes (Swiss-T2)

  11. Remote processingof image data Web browserapplets JNI RMI Machine independentstand-alone application Efficient implementation of processing algorithm Java based client-server applications • Java new programming language is particularly well suited for network based client-server applications

  12.  Node 2 Overlap  Node 3  Node 4 Fundamentals of image processing parallellization • Image processing algorithm may be easily parallelized, either… • splitting the image in tiles, each being processed on a different node  Node 1

  13. Filter Filter  Node 2 Filter Filter  Node 3 Filter  Node 4 Fundamentals of image processing parallellization • Image processing algorithm may be easily parallelized, either… • splitting the image in tiles, each being processed on a different node • splitting an algorithm in independent tasks, each node taking care of its own task (though not all algorithm may be split in such a way)  Node 1

  14. Proteins Microtubules A practical example: the Microtubules application MICROTUBULES ANALYSIS - (c)1998 LTS (C.Dufour) Processing... DONE Computing statistics... DONE Image surface (pixels) : 345600 Microtubules surface (pixels / %) : 97772 / 28.29 Microtubules total length (pixels) : 10347 Microtubules avg. width (pixels) : 9.45 Markers (0) quantity (elmts) : 197 Markers (0) near microtubules (elmts / %) : 126 / 63.00 Markers (0) avg. size (pixels) : 9.61 Markers (1) quantity (elmts) : 0 Markers (1) near microtubules (elmts / %) : 0 / 0.00 Markers (1) avg. size (pixels) : 0.00 • Electron microscopy images analysis application(LTS-IBCM partnership) • Automated statistical analysis of proteins densities, related to microtubules proximity

  15. A practical example: the Microtubules application • The image processing aspect • Extract binary masks representing the interesting structures in the image (segmentation problem)

  16. A practical example: the Microtubules application • Swiss-T0processing times

  17. Conclusions • Biomedical image processing is very demanding on computer power. • Use of supercomputers allows to reduce processing time significantly. • Network based client-server applications allow to take advantage of supercomputer(s) processing power, easily and at lowest cost. • EPFL Swiss-TX andWeb browser basedJavaapplets are an elegant solution for all EPFL and LTS partners. • Early ‘99, a new CTI project will bring LTS, STX Corp., UNIL, CHUC and HUG to collaborate in this new framework.

  18. Thank you for your attention !

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