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)

• 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.

OVERVIEW

• Motivation

• Previous Work

• Comparison Study of Compression Schemes

• ROI based System Design

• Conclusion

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)

OVERVIEW

• Motivation

• Previous Work

• Comparison Study of Compression Schemes

• ROI based System Design

• Conclusion

Lossless Compression• Entropy of images – 7.93bpp

• Predictive Coding – 5.9bpp

• Entropy of difference images – 5.76bpp

DCT Compression (1)

DCT Compression (2)

DCT Compression (3)

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

PCA Compression - Treat each image block as a vector

MSE ~ 30 dB

Rate ~ 0.54 bpp

Blockwise Vector Quantization(1)

- A simpler decoder is required

Blockwise Vector Quantization(2)

MSE ~ 38 dB MSE ~ 39 dB

Motion Compensated Hybrid Coding (1)

- Lukas Kanade Tracker was used by 0.1 pixel accuracy

Lukas-Kanade Tracker

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

OVERVIEW

• Motivation

• Previous Work

• Comparison Study of Compression Schemes

• ROI based System Design

• Conclusion

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

ROI Based System

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

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 dBMSE ~ 33.7 dB

OVERVIEW

• Motivation

• Previous Work

• Comparison Study of Compression Schemes

• ROI based System Design

• Conclusion

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.