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Proceedings Paper

Computerized pectoral muscle identification on MLO-view mammograms for CAD applications
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Paper Abstract

Automatic identification of the pectoral muscle on MLO view is an essential step for computerized analysis of mammograms. It can reduce the bias of mammographic density estimation, will enable region-specific processing in lesion detection programs, and also may be used as a reference in image registration algorithms. We are developing a computerized method for the identification of pectoral muscle on mammograms. The upper portion of the pectoral edges was first detected to estimate the direction of the pectoral muscle boundary. A gradient-based directional (GD) filter was used to enhance the linear texture structures, and then a gradient-based texture analysis was designed to extract a texture orientation image that represented the dominant texture orientation at each pixel. The texture orientation image was enhanced by a second GD filter. An edge flow propagation method was developed to extract edges around the pectoral boundary using geometric features and anatomic constraints. The pectoral boundary was finally generated by a second-order curve fitting. 118 MLO view mammograms were used in this study. The pectoral muscle boundary identified on each image by an experienced radiologist was used as the gold standard. The accuracy of pectoral boundary detection was evaluated by two performance metrics. One is the overlap percentage between the computer-identified area and the gold standard, and the other is the root-mean-square (RMS) distance between the computer and manually identified pectoral boundary. For 118 MLO view mammograms, 99.2% (117/118) of the pectoral muscles could be identified. The average of the overlap percentage is 94.8% with a standard deviation of 20.9%, and the average of the RMS distance is 4.3 mm with a standard deviation of 5.9 mm. These results indicate that the pectoral muscle on mammograms can be detected accurately by our automated method.

Paper Details

Date Published: 29 April 2005
PDF: 6 pages
Proc. SPIE 5747, Medical Imaging 2005: Image Processing, (29 April 2005); doi: 10.1117/12.595693
Show Author Affiliations
Chuan Zhou, Univ. of Michigan (United States)
Lubomir M. Hadjiiski, Univ. of Michigan (United States)
Chintana Paramagul, Univ. of Michigan (United States)
Berkman Sahiner, Univ. of Michigan (United States)
Heang-Ping Chan, Univ. of Michigan (United States)
Jun Wei, Univ. of Michigan (United States)


Published in SPIE Proceedings Vol. 5747:
Medical Imaging 2005: Image Processing
J. Michael Fitzpatrick; Joseph M. Reinhardt, Editor(s)

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