A Large Scale Medical Volume Rendering on Clustering System

1. A Large Scale Medical Volume Rendering on Clustering System Nopparat Pantsaena, Nont Banditwong, Chuchart Pintavirooj, Surapan Airphaiboon and Manas Sangworasil King Mongkut’s Institute of Technology Ladkrabang ,Thailand.

2. Objective This paper exploits a concept of the parallel programming method to accelerate a medically volume rendering process by distributing works to be processed concurrently on each computer in the clustering system.

3. Outline • Introduction • Medical Volume Rendering • Parallel Programming Method and Clustering System • Experiment and Result • Conclusion

4. Introduction

5. Hospital Information System

6. Medical Image Viewer Software 2D Slice Image Sequences Acquisition System

7. Result Images from acquisition system

8. 3D Images 2D Cross Section Slices Reconstruction Process 3D Medical Image Reconstruction

9. Volume Rendering

10. Volume Rendering Operation Preparing Segmentation Gradient Computation Classification Shading Image Compositing

11. Medical Image (CT)

12. Image Slide Preparing

13. Segmentation using Histogram

14. Gradient Computation Central Difference

15. Classification

16. N COSQ = ILN Q L |IL||N| Color and Shading Lambert’s cosine Law I = LCOSQ

17. Image Compositing

18. Output Image

19. Parallel Programming Method and Clustering System

20. Clustering System • Clustering system is the technique of parallel processing that uses several machines communicating with one another via high speed network to process the task. • Clustering system can be implemented from software or library so-called the parallel programming environments such as MPI (Massing Passing Interface) and PVM (Parallel Virtual Machine)

21. Clustering Category • By Hardware structure • Homogeneous Cluster • Heterogeneous Cluster • By Usage • High Performance Cluster (Simulation, 3D Rendering) • High Availability (Web Server, Machine Controller)

22. Clustering Architecture and Parallel Programming Environment

23. MPICH (Message Passing Interface) • We select MPICH to develop the system because it is a portable programming running on a wide variety of parallel computing platforms and it is also a freeware implementation of MPI standard. • MPICH—It can connect between the machine in the clustering system. • MPICH—It can pass themessage thought all process in the system.

24. Segments The Problem

25. Load Balancing Work Pool Scheme

26. Parallel Algorithm Worker Manager Initialize MPI Initialize MPI Pass the origin of each block to worker. Load the image slide into memory Receive output from Worker Receive the origin of block Display the Image Volume Rendering Send the output Image to Manager

27. Experiment And Result

28. Experiment And Result • Our heterogeneous system for testing consists of 6 machines of 2 types • HPx4000 2 Machines. (CPU Pentium 4 Xeon 1.8 GHz RAM 512 MB) • HPx21004 Machines. (CPU Pentium 4 1.9 GHz RAM 256 MB) • Connected with one another via 100 MBPS LAN.

29. Rendering time .....(1)

30. System speedup definite of ideal case. …..(2)

31. System efficiency …… (3)

32. Experimental Result

33. Conclusion This paper focuses on parallelizing the large-scaled volume in volume rendering on clustering system. We have shown the work pool algorithm for load balancing scheme ensuring that all processes are finished at almost the same time. This algorithm can attain high performance on both homogeneous and heterogeneous systems. The experimental result demonstrates the efficiency and practicality of our algorithm. The clustering system can reduce time cost for rendering task.

34. The Testing System

35. Thank you for your attention.