  Breast region segmentation is an essential prerequisite in computerized
analysis of mammograms. It aims at separating the breast tissue from the
background of the mammogram and it includes two independent segmentations. The
first segments the background region which usually contains annotations, labels
and frames from the whole breast region, while the second removes the pectoral
muscle portion (present in Medio Lateral Oblique (MLO) views) from the rest of
the breast tissue. In this paper we propose hybridization of Connected
Component Labeling (CCL), Fuzzy, and Straight line methods. Our proposed
methods worked good for separating pectoral region. After removal pectoral
muscle from the mammogram, further processing is confined to the breast region
alone. To demonstrate the validity of our segmentation algorithm, it is
extensively tested using over 322 mammographic images from the Mammographic
Image Analysis Society (MIAS) database. The segmentation results were evaluated
using a Mean Absolute Error (MAE), Hausdroff Distance (HD), Probabilistic Rand
Index (PRI), Local Consistency Error (LCE) and Tanimoto Coefficient (TC). The
hybridization of fuzzy with straight line method is given more than 96% of the
curve segmentations to be adequate or better. In addition a comparison with
similar approaches from the state of the art has been given, obtaining slightly
improved results. Experimental results demonstrate the effectiveness of the
proposed approach.

Computer Vision and Pattern Recognition;Computational Engineering, Finance, and Science