Two graph theory based methods for identifying the pectoral muscle in mammograms

被引：67

作者：

Ma, Fei

Bajger, Mariusz

Slavotinek, John P.

Bottema, Murk J.

机构：

[1] Flinders Univ S Australia, Sch Informat & Engn, Adelaide, SA 5001, Australia

[2] Flinders Med Ctr, Dept Med Imaging, Bedford Pk, SA 5042, Australia

来源：

PATTERN RECOGNITION | 2007年 / 40卷 / 09期

关键词：

adaptive pyramid; minimum spanning tree; segmentation; pectoral muscle; mammography; computer-aided diagnosis;

D O I：

10.1016/j.patcog.2006.12.011

中图分类号：

TP18 [人工智能理论];

学科分类号：

081104 ; 0812 ; 0835 ; 1405 ;

摘要：

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. (c) 2007 Pattern Recognition Society. Published by Elsevier Ltd. All rights reserved.

引用

页码：2592 / 2602

页数：11