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Content-Based Compression of Mammograms for Telecommunication and Archiving

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

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Content-Based Compression of Mammograms for Telecommunication and Archiving

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  1. 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

  2. Overview • 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

  3. Motivation • 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

  4. Content-Based Compression Approach Lossy Compression 80:1 Background 83% of Image Total Compression 15:1 While Preserving Vital Information Lossless Compression 2:1 FAR 17% of Image

  5. Fractal Analysis Digitized Mammogram or Synthesized Fractal

  6. Fractal Encoding Exact Self-Similarity Partial Self-Similarity

  7. Input Image Quadtree Partition Selected Subset FARs

  8. Microcalcification Coverage

  9. % Data Reduction Pattern

  10. Pilot Study • 80, 12- and 8-bit Mammograms @ 50 Mpixel • 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%

  11. Combination of Compression Techniques Original Image 80:1 Lossy Coding of Background With FARs Removed Superposition of Lossless FARs Over Lossy Background CR=11.54

  12. Combination of Compression Techniques Original Image 80:1 Lossy Coding of Entire Image Superposition of Lossless FARs Over Lossy Image CR=11.54

  13. Preliminary Results

  14. Concluding 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

  15. Concluding 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

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