A fully automated complete segmentation scheme for mammograms

This paper presents a fully automated complete segmentation method for mammographic images. Image preprocessing techniques are first applied to mammograms to remove the noise and then a breast boundary extraction algorithm is implemented, in order to distinguish breast tissue from the background. Next, an improved version of an existing pectoral muscle scheme is performed and a new nipple segmentation technique is applied, detecting the nipple when it is in profile. This improves the estimated breast boundary and serves as a key-point for further processing of the image. This composite method has been implemented and applied to miniMIAS, one of the most well-known mammographic databases. This database consists of 322 mediolateral oblique (MLO) view mammograms, obtained via a digitization procedure. The results are evaluated by an expert radiologist and are very promising. Accordingly, it is expected that this procedure can produce improved results, when applied to high-quality digital mammograms.