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