Transcript A Region of Interest Approach For Medical Image Compression Salih Burak Gokturk

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
Rate ~ 0.54 bpp
MSE ~ 30 dB
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 ~ 33.7 dB
MSE ~ 30.3 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.