Preliminary validation of content-based compression of mammographic images

1. 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

2. Abstract

3. 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

4. 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

5. 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

6. Fractal Analysis Digitized Mammogram or Synthesized Fractal

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

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

9. CBC Software Flow for a Single Sub-Image START Read in Sub-image Perform FAR Generation on Sub-Image Perform Lossy Compression Area Opening Encode FAR Locations and Data Perform Lossless Compression Combine Compression Results END

10. CBC Results

11. CAD System Used for Validation Module 1 Digitized Mammogram Breast Segmentation Convolution Global Thresholding Module 2 Region Growing Local Thresholding Module 3 Screening Result Feature Extraction Labeling The Output of Module 1 is Used for Validation Purposes

12. Application of CAD Module 1 to Original Sub-image Sub-image Result of Convolution Thresholding Result Microcalcifications Have Been Circled for Ease of Viewing

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

14. 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

15. 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

16. 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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