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Detection of Simulated Lesions on Data-compressed Digital Mammograms¹

¹ From the Department of Radiology, Emory University School of Medicine, Winship Cancer Institute, 1701 Uppergate Dr, Bldg C, Suite 5018, Atlanta, GA 30322 (S.S., A.K., S. V., S.M.W., C.J.D.); and the Wallace H. Coulter Department of Biomedical Engineering, Georgia Institute of Technology and Emory University, Atlanta, Ga (S.S., A.K.). Received April 23, 2004; revision requested July 8; revision received August 13; accepted November 8. Supported in part by National Institutes of Health grants RO1-CA88792 from the National Cancer Institute and RO1-EB002123 from the National Institute of Biomedical Imaging and Bioengineering and by a Georgia Cancer Coalition infrastructure grant from the Cancer Scholars Program. Address correspondence to A.K. (e-mail: akarell{at}emory.edu).

PURPOSE: To evaluate retrospectively the effect of a wavelet-based compression method on the detection of simulated masses of various sizes and clustered microcalcifications on data-compressed digital mammograms.

MATERIALS AND METHODS: The images used in this study were acquired with institutional review board approval and patient informed consent, both of which allowed subsequent image data analysis. Patient identification was removed from images, and the study complied with requirements of the Health Insurance Portability and Accountability Act. Masses 3, 6, and 8 mm in diameter were analytically simulated and added to clinical mammographic backgrounds. In addition, microcalcifications were extracted from a clinical mammogram and hybridized with simulated microcalcifications for use in this study. Image compression conditions of 1:1, 15:1, and 30:1 were investigated. Observer responses were recorded with a six-point rating scale, and receiver operating characteristic (ROC) analysis was performed. In addition, two well-established numeric observer models were used to study the effect of image compression under the same compression conditions as were used with human observers. Analysis of variance was performed after observer adjustment to compare the mean values for area under the ROC curve (A_z) across the three compression levels for the masses and microcalcification clusters.

RESULTS: The results of the study indicated no significant differences in the A_z values for masses with the compression conditions investigated. For images of microcalcifications, there were significant differences in A_z values between compression ratios of 1:1 and 30:1 (P = .0005) and of 15:1 and 30:1 (P = .004); the difference between compression ratios of 1:1 and 15:1 was nonsignificant (P = .053). The observer models and human observers exhibited similar trends in detection of the masses investigated in this study.

CONCLUSION: Detection of simulated masses was not affected by the compression method with the conditions used in this study, while the detection of microcalcifications was significantly reduced with a compression ratio of more than 15:1.

© RSNA, 2005

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