1. Signal Processing Laboratory Swiss Federal Institute of Technology, Lausanne 1 Supercomputers and client-server environment for biomedical image processing C. Dufour and J.-Ph. Thiran

2. Signal Processing Laboratory Swiss Federal Institute of Technology, Lausanne 2 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

3. Signal Processing Laboratory Swiss Federal Institute of Technology, Lausanne 3 Contents  Biomedical image processing facts  Supercomputers in a client-server environment.  EPFL’s supercomputer: the Swiss-TX  Java based client-server applications  Fundamentals on image processing parallellization  A practical example: the Microtubules application  Conclusions

4. Signal Processing Laboratory Swiss Federal Institute of Technology, Lausanne 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. Signal Processing Laboratory Swiss Federal Institute of Technology, Lausanne 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. Signal Processing Laboratory Swiss Federal Institute of Technology, Lausanne 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. Signal Processing Laboratory Swiss Federal Institute of Technology, Lausanne 7 Network Image data Supercomputers in a client-server environment  Allow any user (biologist, physician) to take advantage of supercomputer(s) processing power through the network PC Supercomputer Acquisition Viewing Processing Server Client

8. Signal Processing Laboratory Swiss Federal Institute of Technology, Lausanne 8 EPFL’s supercomputer: the Swiss-TX  The Swiss-TX is EPFL’s new supercomputer. Its main characteristics are :

9. Signal Processing Laboratory Swiss Federal Institute of Technology, Lausanne 9 EPFL’s supercomputer: the Swiss-TX  The Swiss-TX is EPFL’s new supercomputer. Its main characteristics are : – Commodity based supercomputer

10. Signal Processing Laboratory Swiss Federal Institute of Technology, Lausanne 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. Signal Processing Laboratory Swiss Federal Institute of Technology, Lausanne 11 Java based client-server applications  Java new programming language is particularly well suited for network based client-server applications JNIRMI Machine independent stand-alone application Web browser applets Remote processing of image data Efficient implementation of processing algorithm

12. Signal Processing Laboratory Swiss Federal Institute of Technology, Lausanne 12 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  Node 3  Node 4  Node 2 Overlap

13. Signal Processing Laboratory Swiss Federal Institute of Technology, Lausanne 13 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) Filter  Node 1 Filter  Node 2 Filter  Node 3 Filter  Node 4

14. Signal Processing Laboratory Swiss Federal Institute of Technology, Lausanne 14 A practical example: the Microtubules application  Electron microscopy images analysis application (LTS-IBCM partnership)  Automated statistical analysis of proteins densities, related to microtubules proximity Proteins Microtubules 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

15. Signal Processing Laboratory Swiss Federal Institute of Technology, Lausanne 15 A practical example: the Microtubules application  The image processing aspect  Extract binary masks representing the interesting structures in the image (segmentation problem)

16. Signal Processing Laboratory Swiss Federal Institute of Technology, Lausanne 16 A practical example: the Microtubules application  Swiss-T0 processing times

17. Signal Processing Laboratory Swiss Federal Institute of Technology, Lausanne 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 and Web browser based Java applets 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. Signal Processing Laboratory Swiss Federal Institute of Technology, Lausanne 18 Thank you for your attention !