1. A Region of Interest Approach For Medical Image Compression Salih Burak Gokturk Stanford University

2. OVERVIEW Motivation Previous Work Comparison Study of Compression Schemes ROI based System Design Conclusion

3. Motivation Medical images are huge.(300x512x512x2) High quality imaging is required in diagnostically important regions. ROI based approach is the only solution: –Lossless compression in ROI. –Very lossy compression in non-ROI.

4. OVERVIEW Motivation Previous Work Comparison Study of Compression Schemes ROI based System Design Conclusion

5. Previous Work Lossless Compression Schemes (Takaya95, Assche00) DCT based Compression Schemes (Vlaciu95) PCA based Compression(Tao96) Wavelet Transformation(2D and 3D) (Baskurt93) ROI based coding (Cosman 94,95)

6. OVERVIEW Motivation Previous Work Comparison Study of Compression Schemes ROI based System Design Conclusion

7. Lossless Compression Entropy of images – 7.93bpp Predictive Coding – 5.9bpp Entropy of difference images – 5.76bpp

8. DCT Compression (1)

9. DCT Compression (2)

10. DCT Compression (3) Quantization Step Size12481632641282565121024 MSE in dB-11.7-5.70.346.2611.917.121.825.729.332.635.9 Rate (without RLC) (bpp) 5.744.974.093.202.341.570.960.550.310.160.09 Rate (with RLC) (bpp) 8.047.095.874.513.151.951.070.550.280.140.07

11. PCA Compression - Treat each image block as a vector MSE ~ 30 dB Rate ~ 0.54 bpp

12. Blockwise Vector Quantization(1) - A simpler decoder is required

13. Blockwise Vector Quantization(2) MSE ~ 38 dB MSE ~ 39 dB

14. Motion Compensated Hybrid Coding (1) - Lukas Kanade Tracker was used by 0.1 pixel accuracy

15. Lukas-Kanade Tracker

16. Motion Compensated Hybrid Coding (2) - Entropy of the motion vector is 2.28 and 2.45 in x and y. - This brings 0.018 bpp. MSE ~ 35 dB

17. OVERVIEW Motivation Previous Work Comparison Study of Compression Schemes ROI based System Design Conclusion

18. Segmentation - Thresholding to find the air - Gradient magnitude to extract the colon wall - Grassfire operation to find the ROI around the colon wall

19. ROI Based System

20. Experiment with 16 by 16 Blocks - The ratio of ROI ~ %12.2 - Entropy of motion vector is 2.28 in x and 2.45 in y - The entropy of the error image is ~ 4.38 - average RMS error 33.7 dB with lossless in ROI - Overall rate 0.552 bps MSE ~ 33.7 dB

21. Experiment with 8 by 8 Blocks - The ratio of ROI ~ %7.3 - Entropy of motion vector is 1.82 in x and 1.96 in y - The entropy of the error image is ~ 4.31 - average RMS error 30.3 dB with lossless in ROI - Overall rate 0.37 bps MSE ~ 30.3 dB MSE ~ 33.7 dB

22. OVERVIEW Motivation Previous Work Comparison Study of Compression Schemes ROI based System Design Conclusion

23. Effective System (compression rate of %2.3) Accurate System (lossless in ROI) Results of ROI based compression over performs standard compression schemes. Future work includes lossy compression in ROI. Case study with the radiologist for determining rate-diagnosis performance curve.