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 – 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

Motivation 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 Approach Lossy Compression 80:1 Lossless Compression 2:1 FAR 17% of Image Background 83% of Image Total Compression 15:1 While Preserving Vital Information

Fractal Analysis Digitized Mammogram or Synthesized Fractal

Input Image Quadtree Partition FARs Selected Subset Microcalcifications Have Been Circled for Ease of Viewing

Combination of Compression Techniques Original Image 80:1 Lossy Coding of Entire Image Superposition of Losslessly Encoded FARs Over Lossy Image CR=11.52 FARs That Will Be Losslessly Encoded

CBC 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 Results

CAD System Used for Validation Region Growing LabelingFeature Extraction Local Thresholding Global Thresholding Breast 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 Sub-image Microcalcifications Have Been Circled for Ease of Viewing Sub-image Result of Convolution Thresholding Result

Application of CAD Module 1 to CBC Sub- image (CR=6.4:1) Microcalcifications Have Been Circled for Ease of Viewing Sub-image Result of Convolution Thresholding Result

Validation 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 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

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

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