Preliminary validation of content-based compression of mammographic images
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Preliminary validation of content-Preliminary validation of content-based compression of mammographic based compression of mammographic
imagesimages
Brad GrinsteadHamed Sari-Sarraf, Shaun Gleason,
and Sunanda Mitra
Funded in part by: National Science Foundation
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AbstractAbstract
This paper presents some preliminary validation results from the content-basedcompression (CBC) of digitized mammograms for transmission, archiving, and,ultimately, telemammography. Unlike traditional compression techniques,CBC is a process by which the content of the data is analyzed before thecompression takes place. In this approach the data is partitioned into twoclasses of regions and a different compression technique is performed on eachclass. The intended result achieves a balance between data compression anddata fidelity. For mammographic images, the data is segmented into two non-overlapping regions: (1) background regions, and (2) focus-of-attentionregions (FARs) that contain the clinically important information.Subsequently, the former regions are compressed using a lossy technique,which attains large reductions in data, while the latter regions are compressedusing a lossless technique in order to maintain the fidelity of these regions. Inthis case, results show that compression ratios averaging 5-10 times greaterthan that of lossless compression alone can be achieved, while preserving thefidelity of the clinically important information.
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OverviewOverview• Objective
– To Make Telemammography More Viable– Decrease Transmission Time – Decrease Storage Requirements
• Concept– Fractal-Based Automatic Data Segmentation
– Divides the Mammogram into 2 regions
• Background Regions• Focus-of-Attention Regions (FARs)
– Combination of Lossy and Lossless Encoding– Decreases Storage Requirements While Preserving Detail
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MotivationMotivation
• When Talking About Compression of Medical Images, There Are Two Camps
– Lossless Compression– Preserves Detail
– Lossy Compression– Reduces Storage Requirements
• Content-Based Compression (CBC) Allows Us to Please Both Camps By Offering More Compression, While Preserving Detail in the Areas of Interest
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Content-Based Compression ApproachContent-Based Compression Approach
Lossy Compression80:1
Lossless Compression2:1
FAR17% of Image
Background83% of Image
Total Compression15:1While
Preserving Vital
Information
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Fractal AnalysisFractal Analysis
Digitized Mammogram or
Synthesized Fractal
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Input Image
Quadtree Partition
FARs
Selected Subset
Microcalcifications Have Been Circled for Ease of Viewing
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Combination of Compression TechniquesCombination of Compression Techniques
Original Image
80:1 Lossy Coding of
Entire Image
Superposition of Losslessly Encoded FARs Over Lossy
ImageCR=11.52
FARs That Will Be Losslessly Encoded
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CBC Software Flow for a Single Sub-ImageCBC Software Flow for a Single Sub-Image
START
Combine Compression Results
Perform Lossless Compression
Perform FAR Generation on Sub-Image
Area Opening
END
Read in Sub-image
Perform Lossy Compression
Encode FAR Locations and Data
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CBC ResultsCBC Results
Threshold
Average Percent of
Image Contained w/in FARs
Average Percent of
Micro- calcifications
Contained w/in FARs
Average Compression
Min Compression
Max Compression
Median Compression
2.0 15.10 82.48% 8.42 2.78 16.84 8.171.9 17.52 88.89% 7.41 2.39 14.69 6.751.8 20.29 93.02% 6.37 2.23 12.50 5.901.7 23.45 95.16% 5.52 2.12 9.96 5.26
Lossless 2.05 1.38 3.28 2.00
Threshold
Average Percent of
Image Contained w/in FARs
Average Percent of
Micro- calcifications
Contained w/in FARs
Average Compression
Min Compression
Max Compression
Median Compression
1.50 11.38 83.13% 18.04 8.55 45.66 14.741.45 13.63 87.86% 15.24 7.44 37.84 12.261.40 16.26 89.09% 12.83 6.23 32.64 10.181.35 19.27 90.95% 10.70 5.35 28.01 8.621.30 22.59 92.18% 9.08 4.70 24.19 7.351.25 26.19 93.00% 7.70 4.17 20.96 6.32
Lossless 1.60 1.42 2.73 1.69
100-micron Data
50-micron Data
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CAD System Used for ValidationCAD System Used for Validation
Region Growing
LabelingFeature Extraction
Local Thresholding
Global ThresholdingBreast Segmentation Convolution
Module 1
Module 2
Module 3
Digitized Mammogram
Screening Result
The Output of Module 1 is Used for Validation Purposes
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Application of CAD Module 1 to Original Application of CAD Module 1 to Original Sub-imageSub-image
Microcalcifications Have Been Circled for Ease of Viewing
Sub-image
Result of Convolution
Thresholding Result
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Application of CAD Module 1 to CBC Sub-Application of CAD Module 1 to CBC Sub-image (CR=6.4:1)image (CR=6.4:1)
Microcalcifications Have Been Circled for Ease of Viewing
Sub-image
Result of Convolution
Thresholding Result
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Validation ResultsValidation Results
• For the Highest Compression Ratio and Lowest Microcalcification Coverage Rate, 93% of the Microcalcifications Were Detected
• For the Lowest Compression Ratio and Highest Microcalcification Coverage Rate, 97%of the Microcalcifications Were Detected
– This shows that the 80:1 compression ratio leaves some of the information outside of FARs intact, while achieving decent compression
– Higher compression ratios will introduce too much distortion, causing microcalcifications outside of FARs to be completely missed
– In addition, context information contained in the background tissue, which is useful to radiologists, has been preserved
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Validation ResultsValidation Results
• The Mammogram That Had the Highest Compression Ratio Also Had the Highest Detection Rate
– This Suggests That There is Not a Direct Relationship Between Microcalcification Detection and the Compression Ratio
Threshold
Average Percent of Image
Contained w/in FARs
Average Percent of Microcalcifications
Contained w/in FARs
Average Percent of Micro-
calcifications Detected by CAD
Module 1
Average Compression
2.0 15.10 82.48% 93.02% 8.421.9 17.52 88.89% 94.44% 7.411.8 20.29 93.02% 96.15% 6.371.7 23.45 95.16% 96.87% 5.52
Lossless 2.05
100-micron Data
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Concluding RemarksConcluding Remarks
• Summary– To Improve the Viability of Telemammography by
Exploring the Following Concepts:– Focus of Attention Regions
• Use the Partial Self-Similarity Inherent in Images to Reduce the Input Data
• Use Quadtree Fractal Encoding to Generate FARs– Content-Based Compression
• Obtain Compression Ratio 5-10 Times Greater Than Lossless Compression Alone, While Preserving the Important Information
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