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

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Presentation on theme: "Visually Lossless Adaptive Compression Of Medical Images David Wu Supervisor Associate Professor Henry Wu."— Presentation transcript:

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 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. Digital Image Compression

7 How Much You Can Hide Away? [M.Chan] 1.2. 3. Visual Masking Example I.Fig 1 is original image, Fig 2 coded image with quantization noise, Fig 3 the error image. II.More quantization noise can be “masked away” in spatially “busy” areas.

8 Digital Image Compression Hierarchical Bit-plane Coding Similar concept to Transform based coders Uses Discrete Wavelet Transform (DWT) for spectral decomposition. Progressive bit-plane quantizer. Three architectures EZW (Embedded Zero tree Wavelet) [Shapiro1993]. SPIHT (Set Partitioning in Hierarchical Trees) [SaP1996]. EBCOT (Embedded Block Coding with Optimized Truncation) [Taubman2000].

9 The Discrete Wavelet Transform Taken from [Wu2002] Advantages over the Discrete Cosine Transform (used in JPEG): [RaY2001] »No blocking artifacts at low bit-rates as seen in JPEG. »Gives good time and frequency localisation. Used in the JPEG2000 standard. Will use the Daubechies 9/7 filters [ABMD1992]. Taken from [Wu2002]

10 Vision Modeling Human 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)

11 Vision Modeling The Human Visual System The human eye – taken from [NEI] Spatial (frequency) contrast sensitivity – taken from [Wu 2002]

12 Vision Modeling The Visual Cortex Masking phenomenon [Tan et. Al. 2003] A signal is hidden or diminished by the presence of another visual signal. Destructive interference. Interactions between different neurons from –Similar (intra-) frequency and orientation. –Different (inter-) frequency and orientation. Facilitation (Negative Masking) Constructive interference.

13 Courtesy of Dr. Van Den Branden Lambretch Signal A Image Signal B Quantization Noise A + B A + B(rotated 90 o )

14 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]

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

16 Vision Modeling Contrast Gain Control Contrast Sensitivity –Contrast sensitivity function represented as a set of uniform weights. –Applied in frequency (transform) domain Masking –A response function, R z »Intra-frequency (  ) »Inter-orientation (  )

17 Vision Modeling Contrast Gain Control Definition of the excitation (E) and inhibition (I) functions for { ,  }:

18 Vision Modeling Contrast 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

19 Proposed Coder Design 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.

20 Proposed Coder Design Process Performed in 4 steps 1.Frequency decomposition. 2.Perceptual filtering. 3.SPIHT [Said et. Al. 1996] encoding. 4.Entropy encoding.

21 Proposed Coder Design Perceptual filtering [Tan et. Al. 2003] 1.Vision model applied  Obtain percentage response, R p and distortion D T. 2.D T and R p are measured respectively against a set of thresholds T D and T P. Where T D and T P are set at the Just-Not-Noticeable- Distortion (JNND) threshold. 3.Filter when R p and D T are below respective threshold. (Signed images required either R p or D T to be below respective threshold).

22 Proposed Coder Design 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).

23 Proposed Coder Calibration –Coder Calibration [Wu et. Al. 2003] –Obtain T D and T P thresholds by testing approximately 5120 (32x32 pixel) sub-images. –Find the Just-Not-Noticeable-Distortion level –Different instruments  Different thresholds –Applicable to unsigned images

24 Proposed Coder Performance 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.

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27 IMAGESIZEMODALITYBIT-DEPTH BIT-RATE (BPP) PCLOCO SPINE512 x 512MRI123.2385.041 CHEST440 x 440CR101.8954.343 LOMB-AN2512 x 512CT121.9385.276 BRAIN512 x 512MRI122.5153.614 BRAIN2512 x 512MRI122.8474.594 BODY1512 x 512CT122.5504.827 BODY2512 x 512CT122.5374.807 BODY3512 x 512CT122.5184.775 BODY4512 x 512CT122.4804.741 BODY5512 x 512CT122.4614.731 KNEE512 x 512CT122.1203.837 BODY6512 x 512CT162.3074.671 Proposed Coder - Performance Evaluation Unsigned Pixel

28 IMAGESIZEMODALITY BIT DEPTH BIT-RATE (BPP) PCLOCO BODY7440 x 440CT161.7952.590 BODY8512 x 512CT164.8765.606 BODY9512 x 512CT164.8625.593 HEAD512 x 512CT163.4974.617 HEAD1440 x 440CT161.3572.548 SKELETON440 x 440CT162.0583.589 Proposed Coder - Performance Evaluation Signed Pixel

29 COMPRESSEDORIGINAL Visually lossless coding of medical images

30 ORIGINALCOMPRESSED

31 ORIGINAL

32 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].

33 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.

34 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.

35 References 1.[Gonzalez 2002] R.C. Gonzalez and R. E. Woods, “Digital Image Processing”. Prentice Hall, Inc., 2 nd edition, 2002. 2.[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 3.[Wu 2002]H. R. Wu. Digital Video Coding and Compression Lecture Notes, 2002. 4.[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/)http://www.nei.nih.gov/photo/

36 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.

37 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. 20.[Erickson 2000] B. J. Erickson, “Irreversible Compression Of Medical Images”, “The Society for Computer Applications in Radiology”, White Paper, November, 2000. 21.[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.

38 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.


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