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Fully automated breast boundary and pectoral muscle segmentation in mammograms

Rampun, Andrik, Morrow, PJ, Scotney, BW and Winder, RJ (2017) Fully automated breast boundary and pectoral muscle segmentation in mammograms. Artificial Intelligence in Medicine, 79 . pp. 28-41. [Journal article]

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URL: http://dx.doi.org/10.1016/j.artmed.2017.06.001

DOI: 10.1016/j.artmed.2017.06.001

Abstract

Breast and pectoral muscle segmentation is an essential pre-processing step for the subsequent processes in Computer Aided Diagnosis (CAD) systems. Esti- mating the breast and pectoral boundaries is a difficult task especially in mam- mograms due to artifacts, homogeneity between the pectoral and breast regions, and low contrast along the skin-air boundary. In this paper, a breast bound- ary and pectoral muscle segmentation method in mammograms is proposed. For breast boundary estimation, we determine the initial breast boundary via thresholding and employ Active Contour Models without edges to search for the actual boundary. A post-processing technique is proposed to correct the overestimated boundary caused by artifacts. The pectoral muscle boundary is estimated using Canny edge detection and a pre-processing technique is pro- posed to remove noisy edges. Subsequently, we identify five edge features to find the edge that has the highest probability of being the initial pectoral con- tour and search for the actual boundary via contour growing. The segmentation results for the proposed method are compared with manual segmentations using322, 208 and 100 mammograms from the Mammographic Image Analysis Soci- ety (MIAS), INBreast and Breast Cancer Digital Repository (BCDR) databases, respectively. Experimental results show that the breast boundary and pectoral muscle estimation methods achieved dice similarity coefficients of 98.8% and 97.8% (MIAS), 98.9% and 89.6% (INBreast) and 99.2% and 91.9% (BCDR), respectively.

Item Type:Journal article
Keywords:Breast mammography; Breast segmentation; Pectoral segmentation; Computer aided diagnosis
Faculties and Schools:Faculty of Computing & Engineering
Faculty of Computing & Engineering > School of Computing and Information Engineering
Faculty of Life and Health Sciences
Faculty of Life and Health Sciences > School of Health Sciences
Research Institutes and Groups:Institute of Nursing and Health Research > Centre for Health and Rehabilitation Technologies
Institute of Nursing and Health Research
Computer Science Research Institute
Computer Science Research Institute > Information and Communication Engineering
ID Code:38191
Deposited By: Professor Philip Morrow
Deposited On:19 Jun 2017 09:51
Last Modified:25 Sep 2018 11:42

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