Breast region segmentation is an essential prerequisite in the (semi-)automatic analysis of digital or digitised mammographic images, which aims to separate the breast region from background information in mammograms. It normally consists of two independent segmentations, which are breast-background segmentation and pectoral muscle segmentation, respectively. The first identifies the boundary between the breast and background, and the second identifies the boundary of the pectoral muscle (present in medio-lateral oblique (MLO) views). In this paper, we propose a method for automatic identification of the breast boundary based on a combination of segmentation approaches, including histogram thresholding, edge detection, contour growing, polynomial fitting, and region growing. To demonstrate the validity of the proposed method, it is tested using two mammographic datasets: the full MIAS database and a large dataset taken from the EPIC database. For the breast-background segmentation, 98.8% and 91.5% nearly accurate results are obtained for the MIAS and EPIC data, respectively. For the pectoral muscle segmentation, 92.8% and 87.9% nearly accurate results are achieved for these two datasets. A comparison with two other methods is also provided based on the full MIAS database. These indicate the proposed method performs effectively in identifying the breast boundary in digitised mammograms. The obtained segmentation results can be used for further analysis in computer-aided diagnosis.
展开▼
