An analysis of local feature extraction in digital mammography

A fundamental problem of automating the detection and recognition of abnormalities in digital mammograms utilizing computational statistics is one of extracting the appropriate features for use in a classification system. Several feature sets have been proposed although none have been shown to be sufficient for the problem. Many of these features tend to be local in nature, which means their calculation requires a connected region of the image over which an average or other statistic is extracted. The implicit assumption is that the region is homogeneous, but this is rarely the case if a fixed window is used for the calculation. We consider a method of using boundaries to segment the window into more homogeneous regions for use in the feature extraction calculation. This approach is applied to the problem of discriminating between tumor and healthy tissue in digital mammography. A set of 21 images, each containing a biopsied mass, is described. The results of the boundary-gated feature extraction methodology on this image set shows a difference in distribution between tissue interior to the mass and tissue far away from the mass. Less difference is discernable when boundaries are not used in the feature extraction.