Computer-Aided Identification of the Pectoral Muscle in Digitized Mammograms

Abstract

Mammograms are X-ray images of human breast which are normally used to detect breast cancer. The presence of pectoral muscle in mammograms may disturb the detection of breast cancer as the pectoral muscle and mammographic parenchyma appear similar. So, the suppression or exclusion of the pectoral muscle from the mammograms is demanded for computer-aided analysis which requires the identification of the pectoral muscle. The main objective of this study is to propose an automated method to efficiently identify the pectoral muscle in medio-lateral oblique-view mammograms. This method uses a proposed graph cut-based image segmentation technique for identifying the pectoral muscle edge. The identified pectoral muscle edge is found to be ragged. Hence, the pectoral muscle is smoothly represented using Bezier curve which uses the control points obtained from the pectoral muscle edge. The proposed work was tested on a public dataset of medio-lateral oblique-view mammograms obtained from mammographic image analysis society database, and its performance was compared with the state-ofthe-art methods reported in the literature. The mean false positive and false negative rates of the proposed method over randomly chosen 84 mammograms were calculated, respectively, as 0.64% and 5.58%. Also, with respect to the number of results with small error, the proposed method out performs existing methods. These results indicate that the proposed method can be used to accurately identify the pectoral muscle on medio-lateral oblique view mammograms.

A Novel Approach for Removal of Pectoral Muscles in Digital Mammogram

“…Camilus etal. 13 proposed a method based on the assumption that mammograms can have curved pectoral muscle edges. In this approach they used graph cut based merging method and a Bezier curve algorithm for pectoral muscle identification.…”

A novel multistage system for the detection and removal of pectoral muscles in mammograms

“…Studies that reveal exact lines using dynamic programming also exist in the literature [16][17][18]22]. A survey of studies utilizing statistical methods exhibits that graph-based approaches have recently been used for pectoral muscle removal [24][25][26]. Li et al and Liu et al modeled a pectoral muscle region as a variable with normal distribution on the basis that a pectoral muscle is more uniform than a breast parenchyma [27,28].…”

Identification and segmentation of obscure pectoral muscle in mediolateral oblique mammograms

“…The classification methods regard the pectoral muscle segmentation as a dichotomous classification problem, that is, each pixel in the mammograms is classified into the target set or the non-target set. 8,19,[35][36][37][38][39][40][41][42][43][44][45][46][47][48][49][50] In addition to the three types of methods, other methods are also proposed, such as discrete cosine transform. 51 Readers can be referred to Ganesan et al 6 for a detailed review on the methods of pectoral muscle segmentation.…”