  Digital mammogram inspection is the most popular technique for early
detection of abnormalities in human breast tissue. When mammograms are analyzed
through a computational method, the presence of the pectoral muscle might
affect the results of breast lesions detection. This problem is particularly
evident in the mediolateral oblique view (MLO), where pectoral muscle occupies
a large part of the mammography. Therefore, identifying and eliminating the
pectoral muscle are essential steps for improving the automatic discrimination
of breast tissue. In this paper, we propose an approach based on anatomical
features to tackle this problem. Our method consists of two steps: (1) a
process to remove the noisy elements such as labels, markers, scratches and
wedges, and (2) application of an intensity transformation based on the Beta
distribution. The novel methodology is tested with 322 digital mammograms from
the Mammographic Image Analysis Society (mini-MIAS) database and with a set of
84 mammograms for which the area normalized error was previously calculated.
The results show a very good performance of the method.
