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Preliminary validation of content- based compression of mammographic images Brad Grinstead Hamed Sari-Sarraf, Shaun Gleason, and Sunanda Mitra Funded in.

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Presentation on theme: "Preliminary validation of content- based compression of mammographic images Brad Grinstead Hamed Sari-Sarraf, Shaun Gleason, and Sunanda Mitra Funded in."— Presentation transcript:

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

6 Fractal Analysis Digitized Mammogram or Synthesized Fractal

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

8 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

9 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

10 CBC Results

11 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

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

13 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

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

17 References The Breast Cancer Resource Center of the American Cancer Society (http://www.cancer.org)http://www.cancer.org S.J. Dwyer III, “PACS Intra and Inter,” 8 th IEEE Symposium on Computer-Based Medical Systems, 1998. M. G. Strintzis, “A Review of Compression Methods for Medical Images in PACS,” Int. J. Med. Inf. 52(1-3), pp. 159-165, 1998. H. P. Chan, et al., “Image Compression in Digital Mammography: Effects on Computerized Detection of Subtle Microcalcifications,” Med. Phys. 23(8), pp. 1324-1336, 1996. R. M. Gray, et al., “Evaluating Quality and Utility in Digital Mammography,” IEEE Int. Conf. on Image Proc., pp. 5-8, October 1996. B. Grinstead, H. Sari-Sarraf, S. Gleason, and S. Mitra, “Content-Based Compression of Mammograms for Telecommunication,” 13 th IEEE Symposium on Computer-Based Medical Systems, pp.37-42, 2000. D. Nister, and C. Christopoulos, “Lossless region of interest coding,” Signal Processing, 78, pp. 1-17, 1999 E.J. Halpern et al., “Application of region of interest definition to quadtree-based compression of CT images,” Investigative Radiology, 25, pp.703-707, June 1990. H. Sari-Sarraf, et al., "A Novel Approach to Computer-Aided Diagnosis of Mammographic Images," 3rd IEEE Workshop on Applications of Comp. Vision, December 1996. H. Li, K.J.R. Liu, and S.-C.B. Lo, “Fractal modeling and segmentation for the enhancement of microcalcifications in digital mammograms,” IEEE Trans. Med. Imaging 16, pp. 785-798, 1997. Y. Fisher, "Fractal image compression with quadtrees," Fractal Compression : Theory and Application to Digital Images, Y. Fisher, ed., pp. 55-77, Springer Verlag, New York, 1994. H. Sari-Sarraf, et al., “Front-End Data Reduction in Computer-Aided Diagnosis of Mammograms: A Pilot Study,” SPIE's Medical Imaging Conf., February 1999. S. Mitra, et al., “High Fidelity Adaptive Vector Quantization at Very Low Bit Rates for Progressive Transmission of Radiographic Images,” J. Electronic Imaging 8(1), 1999, pp. 23-35. J. Shapiro, “Embedded Image Coding Using Zerotrees of Wavelet Coefficients,” Transactions on Signal Processing, 41(12), December 1993, pp. 3445-3462. A. Said and W. Perlman, “A New, Fast, and Efficient Image Codec Based on Set Partitioning in Hierarchical Trees,” IEEE Transactions on Circuits and Systems for Video Technology, 6(3), pp. 243-249, June 1996. P.G. Howard, and J.S. Vitter, “Arithmetic Coding for Data Compression,” Proceedings of the IEEE, 82(6), June 1994. H.P. Chan, et al., “Improvement in radiologists’ detection of microcalcifications on mammograms: The potential of computer-aided diagnosis,” Investigative Radiology, 25 pp. 1102-1110, 1990. S. S. Gleason, H. Sari-Sarraf, K. T. Hudson, and K. F. Hubner, “Higher accuracy and throughput in computer-aided screening of mammographic microcalcifications,” IEEE Medical Imaging Conf., 1997.


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