Abstract. We studied the effectiveness of using texture features derived from spatial grey level dependence (SOLD) matrices for classification of masses and n o d breast tissue on mammograms. One hundred and sixtyeight regions of interest (~01s) containing biopsyproven masses and 504 ROE containing normal breast tissue were extracted from digitized mammograms for this study. Eight features were calculated for each no!. The impfiance of each feahm in distinguishing masses from normal tissue was determined by stepwise linear discriminant analysis. Receiver operating characteristic (ROC) methodology was used to evaluate the classification accuracy. We investigated the dependence of classification accuracy on the input features, and on the pixel distance and bit depth in the construction of the SOW matrices. It was found that five of the texture features were important for the classification. The dependence of classification accuracy on distance and bit depth was weak for distances greater than 12 pixels and bit depths greater than seven bits. By randomly and equally dividing the data set into WO groups. the classifier was trained and tested on independent d a h sets. The classifier achieved an average area under the ROC curve, A,, of 0.84 during training and 0.82 during testing. The results demonsate the feasibility of using linear discriminant analysis in thttexture feature space for classification of true and false detections of masses on mammograms in a computer-aided diagnosis scheme.
Chan et al. REF studied the importance of Texture features derived from GLCM matrix for classification of masses.
Computer-aided classification of mammographic masses and normal tissue: linear discriminant analysis in texture feature space
Physics in medicine and biology
Physics in medicine and biology
Mathematics Medicine