Improving the Detection of Simulated Masses in Mammograms ...
Content-Based Compression of Mammograms for Telecommunication and Archiving
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Content-Based Compression Content-Based Compression of Mammograms for of Mammograms for Telecommunication and Telecommunication and Archiving Archiving Brad Grinstead Hamed Sari-Sarraf, Shaun Gleason, and Sunanda Mitra In Collaboration With: Lockheed Martin Energy Systems, Oak Ridge National Laboratories, and University of Chicago
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Content-Based Compression of Mammograms for Telecommunication and Archiving. Brad Grinstead Hamed Sari-Sarraf, Shaun Gleason, and Sunanda Mitra In Collaboration With: Lockheed Martin Energy Systems, Oak Ridge National Laboratories, and University of Chicago. Overview. Objective - PowerPoint PPT Presentation
Transcript of Content-Based Compression of Mammograms for Telecommunication and Archiving
Content-Based Compression of Content-Based Compression of Mammograms for Telecommunication Mammograms for Telecommunication
and Archivingand Archiving
Brad GrinsteadHamed Sari-Sarraf, Shaun Gleason,
and Sunanda Mitra
In Collaboration With: Lockheed Martin Energy Systems, Oak Ridge
National Laboratories, and University of Chicago
OverviewOverview• Objective
– To Make Telemammography More Viable– Decrease Transmission Time – Decrease Storage Requirements– Increase Throughput of Computer Aided Diagnosis
• Concept– Fractal-Based, Front-End Data Reduction
– Reduces Input Data/False Detections
– 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
• CBIC 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
Fractal EncodingFractal Encoding
Exact Self-Similarity Partial Self-Similarity
Input Image
Quadtree Partition
FARs
Selected Subset
Microcalcification CoverageMicrocalcification Coverage
% Data Reduction Pattern
Pilot StudyPilot Study
• 80, 12- and 8-bit Mammograms @ 50 Mpixel• Increased Pixel Depth Did Not Impact Results• 83% Reduction in Input Data (64% to 94%)• 86% Reduction in False Detections (2984 to 407
Detections Per Image)• 467 Out of 507 Calcifications Included in FARs for a
Coverage Rate of 92%
Combination of Compression TechniquesCombination of Compression Techniques
Original Image 80:1 Lossy Coding of
Background With FARs Removed
Superposition of Lossless FARs Over Lossy Background
CR=11.54
Combination of Compression TechniquesCombination of Compression Techniques
Original Image 80:1 Lossy Coding of
Entire Image
Superposition of Lossless FARs Over
Lossy ImageCR=11.54
Preliminary ResultsPreliminary Results
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
Concluding RemarksConcluding Remarks
• Ongoing Efforts– Efficient Coding of FARs– Selection of Appropriate Compression Techniques– CBIC on Entire Mammogram Sets– Tuning of the Fractal Encoding Process for Mammogram
Images– Selection of Appropriate Classification Scheme– Selection of Appropriate Dissimilarity Metric– Selection of Appropriate Partitioning Scheme
