Classification of benign and malignant masses in mammograms using multi-resolution analysis of oriented patterns

The paper proposes a novel approach for the classification of breast masses as benign and malignant using multi-resolution analysis of oriented patterns of tissues in mammograms. Since, the oriented structures of normal breast near the mass region may be changed in presence of masses, three regions are defined, first, for the analysis. Statistical features are then extracted using two angle co-occurrence matrices derived at different resolution levels of each region with Haar-wavelet transform to quantify the joint occurrences of different angle pairs of oriented patterns. The experiments show best classification accuracy of 81.23% and area under the receiver operating characteristic curve of 0.86 with 433 images from the DDSM database using artificial neural network and tenfold cross-validation method.