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Preliminary validation of content-based compression of mammographic images. Brad Grinstead Hamed Sari-Sarraf, Shaun Gleason, and Sunanda Mitra Funded in part by: National Science Foundation. Abstract. Overview. Objective To Make Telemammography More Viable - PowerPoint PPT Presentation
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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
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.
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
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
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
Fractal AnalysisFractal Analysis
Digitized Mammogram or
Synthesized Fractal
Input Image
Quadtree Partition
FARs
Selected Subset
Microcalcifications Have Been Circled for Ease of Viewing
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
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
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
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
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
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
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
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
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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