Segmentation of suspicious densities in digital mammograms.

State-of-the-art algorithms for detection of masses in mammograms are very sensitive but they also detect many normal regions with slightly suspicious features. Based on segmentations of detected regions, shape and intensity features can be computed that discriminate between normal and abnormal regions. These features can be used to discard false positive detections and hence improve the specificity of the detection method. In this work two different methods to segment suspect regions were examined. A number of different implementations of a region growing method were compared to a discrete dynamic contour method. Both methods were applied to a consecutive data set of 132 mammograms containing masses and architectural distortions, taken from the Dutch screening program. Evaluation of the performance of the methods was done in two different ways. In the first experiment, the segmentations of masses were compared to annotations made by the radiologist. In the second experiment, a number of features were computed for all segmented areas, normal and abnormal, based on which regions were classified with a neural network. The most sophisticated region growing method and the method using the dynamic contour model had a similar performance when evaluation was based on the overlap of the annotations. The second experiment showed that the contours generated by the discrete dynamic contour model were more suited for computation of discriminating features. Contrast features were especially useful to improve the performance of the detection method.