Visually Lossless Adaptive Compression Of Medical Images David Wu Supervisor Associate Professor...
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Visually Lossless Adaptive Compression Of Medical Images
David Wu
Supervisor
Associate Professor Henry Wu
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.
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.
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.
The Roadmap
• Digital image compression
• Vision modeling
• Proposed coder
• Conclusion
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
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.
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)
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]
Vision ModelingContrast Gain Control
• Will adopt the contrast gain control model (CGC) of Watson and Solomon [WaS1997].
[TWY2003]
2,, || zbza
zzT RRgD
Signal AImage
Signal BQuantisation
noise
A+BA+B
(rotate B 90o)
Courtesy of Dr. Van Den Branden Lambretch
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 ()
qzz
zz I
ER
},{ z
Vision ModelingContrast Gain Control
• Definition of the excitation (E) and inhibition (I) functions for {,}:
2
],,[1
],,[8
{1,2,3} ],,[
],,[
],,[
var
var
lm
lmu
ln
lnvl
q
qlm
lmu
ln
lnv
ql
ql
pl
pl
vuXN
vuXN
I
nmXI
nmXE
nmXE
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
2,, || zbza
zzT RRgD
0 qpz
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.
Proposed CoderDesign
• Process• Performed in 4 steps
1. Frequency decomposition.
2. Perceptual filtering.
3. SPIHT [Said et. Al. 1996] encoding.
4. Entropy encoding.
Proposed CoderDesign
• Perceptual filtering [Tan et. Al. 2003]1. Vision model applied Obtain percentage response, Rp and
distortion DT.
2. 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.
3. Filter when Rp and DT are below respective threshold. (Signed images required either Rp or DT to be below respective threshold).
RR
RRRp
2,, || zbza
zzT RRgD
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).
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
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.
Compression Ratio (PC VS LOCO)Unsigned
0
1
2
3
4
5
6
Spine Chest Lomb-an2
Brain Brain2 Body1 Body2 Body3 Body4 Body5 Knee Body6
Image
Co
mp
res
sio
n R
ati
o (
bp
p)
PC
LOCO
Compression Ratio (PC VS LOCO) Signed
0
1
2
3
4
5
6
Body7 Body8 Body9 Head Head1 Skeleton
Images
Co
mp
ress
ion
Rat
io (
bp
p)
PC
LOCO
IMAGE SIZE MODALITY BIT-DEPTHBIT-RATE (BPP)
PC LOCO
SPINE 512 x 512 MRI 12 3.238 5.041
CHEST 440 x 440 CR 10 1.895 4.343
LOMB-AN2 512 x 512 CT 12 1.938 5.276
BRAIN 512 x 512 MRI 12 2.515 3.614
BRAIN2 512 x 512 MRI 12 2.847 4.594
BODY1 512 x 512 CT 12 2.550 4.827
BODY2 512 x 512 CT 12 2.537 4.807
BODY3 512 x 512 CT 12 2.518 4.775
BODY4 512 x 512 CT 12 2.480 4.741
BODY5 512 x 512 CT 12 2.461 4.731
KNEE 512 x 512 CT 12 2.120 3.837
BODY6 512 x 512 CT 16 2.307 4.671
Proposed Coder - Performance EvaluationUnsigned Pixel
IMAGE SIZE MODALITYBIT
DEPTH
BIT-RATE (BPP)
PC LOCO
BODY7 440 x 440 CT 16 1.795 2.590
BODY8 512 x 512 CT 16 4.876 5.606
BODY9 512 x 512 CT 16 4.862 5.593
HEAD 512 x 512 CT 16 3.497 4.617
HEAD1 440 x 440 CT 16 1.357 2.548
SKELETON 440 x 440 CT 16 2.058 3.589
Proposed Coder - Performance EvaluationSigned Pixel
COMPRESSED ORIGINAL
Visually lossless coding of medical images
ORIGINALCOMPRESSED
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].
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.
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.
References1. [Gonzalez 2002] R.C. Gonzalez and R. E. Woods, “Digital Image
Processing”. Prentice Hall, Inc., 2nd 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/)
References6. [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.
References12. [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.
References22. [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.