Scale focusing of statistical shape models for breast region segmentation and pectoral muscle suppression

Abstract

Automated image analysis is used for risk assessment and computer aided diagnosis of breast cancer. A prerequisite for this automation is an efficient and robust segmentation of the region of interest (ROI). Extraction of breast without the pectoral muscle, the ROI in our study, from mammograms is a challenging task due to the tapering nature of the breast at the skin-air interface and the overlap between the high density regions and the pectoral muscle in the medio-lateral oblique (MLO) views. To segment breast skin-air interface, Otsu's multilevel threshold based algorithm constrained with a shape prior is used. Starting at a coarse scale, the pectoral muscle is detected by fitting a novel adaptive statistical shape model at the top left corner in the medio-lateral oblique views of mammograms and its position is localized by scale focusing. A novel energy term is proposed to fit the shape model. The proposed algorithm is applied to a set of 50 mammograms as a post hoc analysis of an earlier trial. The results are evaluated by comparing with manual annotations. In the pectoral muscle detection the metrics used for the evaluation are: (a) mean Hausdorff distance (MHD):6.7 pixels; (b) area overlap:87%; (c) false positive rate (FP):0.7%; (d) false negative rate (FN):12.1% and (e) mean relative breast area error (MRBAE):101.19±5.26 %.