Visually Lossless Adaptive Compression Of Medical Images

1. Visually Lossless Adaptive Compression Of Medical Images David Wu Supervisor Associate Professor Henry Wu

2. Why Medical Compression? • Digital medical imaging. [Gonzalez 2002] • Sources: [Archarya et. Al. 1995] • Computed axial tomography (CAT or CT for short). • Magnetic resonance (MRI) imaging. • Ultrasound, Mammography and Computed Radiography (CR). • Advantages: • Faster and accurate diagnoses. • Permanent storage, resistant to heat and moisture. • Applications • Telemedicine.

3. Why Medical Compression? • Digital medical imaging (cont.) • Costs: [Strintzis 1998] • Large transmission bandwidth. • Large storage space. • Estimate of 1 million images per annum, 2 terabytes storage capacity.

4. Focus • A solution is image compression • Mainly two broad categories: [Gonzalez 2002, Erickson 2000] • Reversible (lossless) compression. • Irreversible (lossy) compression. • Perceptually lossless coding • Produce images without any visible distortions. • Compression improvements over lossless techniques. • Goal • Implement, extend and calibrate a vision based, perceptually lossless coder from a previous work by Tan et. Al. [Tan 2003] for medical images.

5. The Roadmap • Digital image compression • Vision modeling • Proposed coder • Conclusion

6. Digital Image Compression Statistical and Psycho-visual redundancies of images • Statistical redundancies: • Coding redundancy; • Spatial redundancy between pixels within a single frame of image • Neighbouring pixels are highly correlated. • Psycho-visual Redundancy: • Redundancies the human eye cannot see.

7. How Much You Can Hide Away?[M.Chan] 1. 2. Visual Masking Example 3. • Fig 1 is original image, Fig 2 coded image with quantization noise, Fig 3 the error image. • More quantization noise can be “masked away” in spatially “busy” areas.

8. Vision ModelingHuman Visual System • Remove redundant visual information • What is visually relevant or irrelevent • Mathematical measures such as PSNR and MSE may not correlate well with what is perceived. • Model the human visual system (HVS)

9. Vision Modeling Vision Models • HVS modelled in three stages [Watson et. Al. 1997] • Past research and models • Legge and Foley’s model [Legge et. Al. 1980] • Teo and Heeger’s model [Teo et. Al. 1994, Teo et. Al. 1994 :2 ] • Watson and Solomon’s model [Watson et. Al. 1997] The Contrast Gain Control, coined by Watson and Solomon [Watson et. Al. 1997]

10. Signal B Quantisation noise Signal A Image A+B A+B (rotate B 90o) Courtesy of Dr. Van Den Branden Lambretch Vision ModelingContrast Gain Control • Will adopt the contrast gain control model (CGC) of Watson and Solomon [WaS1997]. [TWY2003]

11. Vision ModelingContrast Gain Control • Contrast Sensitivity • Contrast sensitivity function represented as a set of uniform weights. • Applied in frequency (transform) domain • Masking • A response function, Rz • Intra-frequency () • Inter-orientation ()

12. Vision ModelingContrast Gain Control • Definition of the excitation (E) and inhibition (I) functions for {,}:

13. Vision ModelingContrast Gain Control • Detection and pooling: • Response of two images, a and b. z {,} • Gives the total perceptually significant difference. • q is set to 2, as in [Watson et. Al. 1997], where

14. Proposed CoderDesign • Structure • Implement vision model, closely following Tan et. Al. [Tan et Al. 2003]. (perceptual filtering) • Based on the SPIHT [Said et. Al. 1996] framework of Said and Pearlman for simplicity. • Perceptual filtering is performed only during the encoding phase.

15. Proposed CoderDesign • Process • Performed in 4 steps • Frequency decomposition. • Perceptual filtering. • SPIHT [Said et. Al. 1996] encoding. • Entropy encoding.

16. Proposed CoderDesign • Perceptual filtering [Tan et. Al. 2003] • Vision model applied  Obtain percentage response, Rp and distortion DT. • DT and Rp are measured respectively against a set of thresholds TD and TP. Where TD and TP are set at the Just-Not-Noticeable-Distortion (JNND) threshold. • Filter when Rp and DT are below respective threshold. (Signed images required either Rp or DT to be below respective threshold).

17. Proposed CoderDesign • Perceptual filtering Applying perceptual filtering through progressive bit-plane masking of transform coefficients. From the least significant bit (lsb) upwards. • Applied to all decomposition levels, except the isotropic (DC) band (level 1).

18. Proposed CoderCalibration • Coder Calibration [Wu et. Al. 2003] • Obtain TD and TP thresholds by testing approximately 5120 (32x32 pixel) sub-images. • Find the Just-Not-Noticeable-Distortion level • Different instruments  Different thresholds • Applicable to unsigned images

19. Proposed CoderPerformance Evaluation • Compared to the LOCO (JPEG-LS) • Superior to LOCO [Weinberger et. Al. 1996] in all instances. • Performance appears better for CT than CR and MR images. • More importantly, no visual impairments.

22. Proposed Coder - Performance EvaluationUnsigned Pixel

23. Proposed Coder - Performance EvaluationSigned Pixel

24. Visually lossless coding of medical images COMPRESSED ORIGINAL

25. COMPRESSED ORIGINAL

26. Conclusion • Medical Imaging - An increasing demand • High resolution images. • Life time storage. • Telemedicine. • Image Compression – Vision based coders • Reversible and irreversible compression. • Removes perceptually insignificant information. • Proposed Coder – Perceptually Lossless [Wu et. Al. 2003, Wu et. Al. 2003:2] • SPIHT framework embedded with a vision model. • Designed for various unsigned and signed pixel represented medical images (CT, CR, MR, etc). • Compressed image has no visual distortion. • Superior compression ratio over the LOCO coder. [Weinberger 1996].

27. Future Work • Require subjective assessment from radiologist, sonographers, radiographers and so forth. • Other human vision properties • Object and pattern recognition • Vision model optimization • Extend the work to the JPEG2000 coding engine.

28. Acknowledgements • I would like to thank Dr. Tan and Associate Professor Wu (without any order) for all their time and patience for helping me throughout the year.

29. References • [Gonzalez 2002] R.C. Gonzalez and R. E. Woods, “Digital Image Processing”. Prentice Hall, Inc., 2nd edition, 2002. • [Strintzis 1998] M. G. Strintzis, “A review of compression methods for medical images in PACS”, “International Journal of medical informatics”, No. 52, Pg 159-165, 1998 • [Wu 2002] H. R. Wu. Digital Video Coding and Compression Lecture Notes, 2002. • [Archarya 1995] R. Acharya, R. Wasserman, J. Stevens and C. Hinojosa “Biomedical imaging modalities: A tutorial”, “Compterized Medical Imaging and Graphics”, Vol 19, No. 1, pg 3-25,1995 5. [NEI] National Eye Institute, (http://www.nei.nih.gov/photo/)

30. References 6. [Watson et. Al. 1997]A.B. Watson and J.A.Solomon, “A model of visual contrast gain control and pattern masking”. Journal Of Optical Society Of America, Pages 2379-2391, 1997. 7. [Teo et Al. 1994] P.C. Teo and D. J.Heeger. “Perceptual Image Distortion”.Proceedings of SPIE, 2179:127 –141, 1994. 8. [Teo et Al. 1994:2] P.C. Teo and D. J.Heeger. “Perceptual Image Distortion”. In Proc. Of IEEE Int. Conf. On Image Processing 2:982-986, November 1994. 9. [Said et. Al. 1996] A. Said and W.A. Pearlman, “A new fast and efficient image codec based on Set Partitioning in Hierarchical Trees”, IEEE Transaction on Circuits and Systems for Video Technology, 6(1), June 1996. 10. [Tan et. Al. 2003] D.M. Tan and H.R. Wu and ZhengHua Yu, “Perceptual Coding of Digital Monochrome Images”, to appear in IEEE Signal Processing Letters, 2003. 11. [Weinberger 1996] M. Weinberger, G. Seroussi, and G. Sapiro, “LOCO-I: A Low Complexity, Context-Based, Lossless Image Compression Algorithm,” in Proceedings of IEEE Data Compression Conference, pg 29-31, Oct-Nov 1997.

31. References 12. [Said et. Al. 1996] A. Said and W.A. Pearlman, “A new fast and efficient image codec based on Set Partitioning in Hierarchical Trees”, IEEE Transaction on Circuits and Systems for Video Technology, 6(1), June 1996. 13. [Tan et. Al. 2003] D.M. Tan and H.R. Wu and ZhengHua Yu, “Perceptual Coding of Digital Monochrome Images”, to appear in IEEE Signal Processing Letters, 2003. 14. [Weinberger 1996] M. Weinberger, G. Seroussi, and G. Sapiro, “LOCO-I: A Low Complexity, Context-Based, Lossless Image Compression Algorithm,” in Proceedings of IEEE Data Compression Conference, pg 29-31, Oct-Nov 1997. • [Erickson 2000] B. J. Erickson, “Irreversible Compression Of Medical Images”, “The Society for Computer Applications in Radiology”, White Paper, November, 2000. • [Legge et. Al. 1980] G. E. Legge and J. M. Foley, “Contrast Masking In Human Vision”, “Journal of the Optical Society Of America”. Vol. 70, No. 12, pg 1458-1471, December, 1980.

32. References 22. [Wu et. Al. 2003] D. Wu ,D.M. Tan and H. R. Wu, “A vision model based approach to medical image compression”, “International Seminar on Consumer Electronics”. Sydney, December 3-5, 2003, In Press. 23. [Wu et. Al. 2003:2] D. Wu ,D.M. Tan and H. R. Wu, “Visually Lossless Adaptive Compression Of Medical Images”, “Fourth International Conference on Information, Communications & Signal Processing and Fourth Pacific-Rim Conference on Multimedia (ICICS-PCM 2003)”. Singapore, December 15-18, 2003, In Press.