Digital mammography: a weak continuity texture representation for detection of microcalcifications

This paper proposes a Weak Continuity Texture Representation (WCTR) method for detecting clustered microcalcifications in digitized mammograms. This technique is compared with other texture-analysis methods (Co-occurrence Matrices, Gabor Energy Mask, and Wavelet Filter). The WCTR is a new method for texture representation, based on the characterization of textures using statistics of their coarseness. Form edge maps, obtained by a weak membrane at different noise levels, density values are computed which are representative of the texture coarseness. We chose six different noise levels; each texture class is then represented by six edge-density values. Textural features extracted using the four methods are used to discriminate between positive ROI's containing clustered microcalcifications and negative ROI's containing normal tissue; a three-layer backpropagation neural network is employed as a classifier. A ROC analysis is used to evaluate the classification performance. From an original database of 151 ROIs two different combinations of training and testing sets are used: 50/70 training cases and 101/81 testing cases. The best performance is obtained with the WCTR method in both cases (92% and 93% respectively). These results show the effectiveness of WCTR for the detection of microcalcifications in mammographic images.