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A Region of Interest Approach For Medical Image Compression

A Region of Interest Approach For Medical Image Compression. Salih Burak Gokturk Stanford University. OVERVIEW. Motivation Previous Work Comparison Study of Compression Schemes ROI based System Design Conclusion. Motivation. Medical images are huge.(300x512x512x2)

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A Region of Interest Approach For Medical Image Compression

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  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. Quantization  Step Size 1 2 4 8 16 32 64 128 256 512 1024 MSE in dB -11.7 -5.7 0.34 6.26 11.9 17.1 21.8 25.7 29.3 32.6 35.9 Rate (without RLC) (bpp) 5.74 4.97 4.09 3.20 2.34 1.57 0.96 0.55 0.31 0.16 0.09 Rate (with RLC) (bpp) 8.04 7.09 5.87 4.51 3.15 1.95 1.07 0.55 0.28 0.14 0.07 DCT Compression (3)

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

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

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

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