Feature extraction in digital mammograms based on optimal and morphological filtering

The aim of this study is to provide feature images of digital mammograms in which regions corresponding to masses are enhanced. Subsequently. the feature images can be segmented and classified into two classes; masses and normal tissue. Our proposed feature extraction method is based on a local energy measure as texture feature. The local energy measure is extracted using a filter optimized with respect to the relative distance between the average feature values. In order to increase the sensitivity, of the texture feature extraction scheme each mammograrn is preprocessed using wavelet transformation, adaptive histogram equalization, and a morphology based enhancement technique. Initial experiments indicate that our scheme is able to provide useful feature images of digital mammograms. In order to quantify the system performance the feature images of 38 mammograms from the MIAS database - 19 containing circumscribed masses, and 19 containing spiculated masses - were segmented using simple gray level thresholding. For the circumscribed masses a true positive (TP) rate of 89% with a corresponding 2.3 false detections (false positives, FPs) per image was achieved. For the spiculated masses the performance was somewhat lower, yielding an overall TP rate of 84% with a corresponding 2.6 FPs per image.