Two graph theory based methods for identifying the pectoral muscle in mammograms
Pattern Recognition, 2007•Elsevier
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 2mm. In 82 of 84 images, the boundary of the pectoral …
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 2mm. In 82 of 84 images, the boundary of the pectoral …
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 2mm. In 82 of 84 images, the boundary of the pectoral muscle found by the AP algorithm has average error less than 5mm.
Elsevier
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