Two graph theory based methods for identifying the pectoral muscle in mammograms |
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Authors: | Fei Ma [Author Vitae] [Author Vitae] John P. Slavotinek [Author Vitae] [Author Vitae] |
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Affiliation: | a School of Informatics and Engineering, Flinders University, P.O. Box 2100, Adelaide, SA 5001, Australia b Department of Medical Imaging, Flinders Medical Centre, Bedford Park, SA 5042, Australia |
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Abstract: | Two image segmentation methods based on graph theory are used in conjunction with active contours to segment the pectoral muscle in screening mammograms. One method is based on adaptive pyramids (AP) and the other is based on minimum spanning trees (MST). The algorithms are tested on a public data set of mammograms and results are compared with previously reported methods. In 80% of the images, the boundary of the segmented regions has average error less than 2 mm. In 82 of 84 images, the boundary of the pectoral muscle found by the AP algorithm has average error less than 5 mm. |
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Keywords: | Adaptive pyramid Minimum spanning tree Segmentation Pectoral muscle Mammography Computer-aided diagnosis |
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