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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

  • Salih Burak Gokturk

  • Stanford University


OVERVIEW

  • Motivation

  • Previous Work

  • Comparison Study of Compression Schemes

  • ROI based System Design

  • Conclusion


Motivation
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
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
Lossless Compression

  • Entropy of images – 7.93bpp

  • Predictive Coding – 5.9bpp

  • Entropy of difference images – 5.76bpp




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

DCT Compression (3)


Pca compression
PCA Compression

- Treat each image block as a vector

Rate ~ 0.54 bpp

MSE ~ 30 dB


Blockwise vector quantization 1
Blockwise Vector Quantization(1)

- A simpler decoder is required


Blockwise vector quantization 2
Blockwise Vector Quantization(2)

MSE ~ 39 dB

MSE ~ 38 dB


Motion compensated hybrid coding 1
Motion Compensated Hybrid Coding (1)

- Lukas Kanade Tracker was used by 0.1 pixel accuracy



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
Segmentation

  • Thresholding to find the air

  • Gradient magnitude to extract the colon wall

  • Grassfire operation to find the ROI around the colon wall



Experiment with 16 by 16 blocks
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
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


OVERVIEW

  • Motivation

  • Previous Work

  • Comparison Study of Compression Schemes

  • ROI based System Design

  • Conclusion


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.


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