Automatic Pectoral Muscle Segmentation in Full-Field Digital Mammography Images Using Log-Gabor Filters

Abstract

Mammography image segmentation is one of the first steps taken by a computer-aided diagnosis system, to find the region of interest for further processing and risk assessment. Failure to do so properly could lead to false positives (identifying cancer when there is none) or false negatives (failure to identify cancer), both of which are not good outcomes.

Although many unique segmentation methods exist already, many of them are either slow to compute or not able to adapt to the variety of different mammograms. In this thesis a new method is proposed, based on log Gabor filters. In the method, two log Gabor filter banks are used for the extraction of the muscle edge: a preliminary filter bank to identify the approximate orientation of the muscle edge, then the primary filter bank is used for identifying the pectoral muscle edge. The method is simple, quite fast and works well in a variety of mammograms.