Texture based classification of mass abnormalities in mammograms

This paper presents a scheme for the classification of mass abnormalities in digitized or digital mammograms based on two novel image texture features. The first texture feature provides a measure of smoothness/denseness and is obtained by applying a morphological operator to maxima/minima image points. The second texture feature reflects a measure of architectural distortion and is derived from image gradients. A three-layer backpropagation neural network is used as the classifier. The performance of the classification scheme is evaluated by carrying our a receiver operating characteristic (ROC) analysis. Classification of 150 biopsy proven masses into benign and malignant classes resulted in a ROC area of 0.91. The results obtained demonstrate the potential of using this scheme as an electronic second opinion to lower the number of unnecessary biopsies.