  Currently Mammography is a most effective imaging modality used by
radiologists for the screening of breast cancer. Finding an accurate, robust
and efficient breast region segmentation technique still remains a challenging
problem in digital mammography. Extraction of the breast profile region and the
removal of pectoral muscle are essential pre-processing steps in Computer Aided
Diagnosis (CAD) system for the diagnosis of breast cancer. Primarily it allows
the search for abnormalities to be limited to the region of the breast tissue
without undue influence from the background of the mammogram. The presence of
pectoral muscle in mammograms biases detection procedures, which recommends
removing the pectoral muscle during mammogram image pre-processing. The
presence of pectoral muscle in mammograms may disturb or influence the
detection of breast cancer as the pectoral muscle and mammographic parenchymas
appear similar. The goal of breast region extraction is reducing the image size
without losing anatomic information, it improve the accuracy of the overall CAD
system. The main objective of this study is to propose an automated method to
identify the pectoral muscle in Medio-Lateral Oblique (MLO) view mammograms. In
this paper, we proposed histogram based 8-neighborhood connected component
labelling method for breast region extraction and removal of pectoral muscle.
The proposed method is evaluated by using the mean values of accuracy and
error. The comparative analysis shows that the proposed method identifies the
breast region more accurately.
