Computer-aided classification of mammographic masses and normal tissue: linear discriminant analysis in texture feature space

TLDR

The study examined whether texture features derived from spatial grey level dependence matrices could classify masses versus normal breast tissue on mammograms. The authors extracted eight SGLD‑derived texture features from 168 mass and 504 normal ROIs, applied stepwise linear discriminant analysis with ROC evaluation, and trained the classifier on one half of the data while testing on the other. Five features were most discriminative, and the classifier achieved AUCs of 0.84 (training) and 0.82 (testing), showing that linear discriminant analysis in texture feature space can effectively distinguish masses from normal tissue.

Abstract

We studied the effectiveness of using texture features derived from spatial grey level dependence (SGLD) matrices for classification of masses and normal breast tissue on mammograms. One hundred and sixty-eight regions of interest (ROIS) containing biopsy-proven masses and 504 ROIS containing normal breast tissue were extracted from digitized mammograms for this study. Eight features were calculated for each ROI. The importance of each feature 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 SGLD 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 two groups, the classifier was trained and tested on independent data sets. The classifier achieved an average area under the ROC curve, Az, of 0.84 during training and 0.82 during testing. The results demonstrate the feasibility of using linear discriminant analysis in the texture feature space for classification of true and false detections of masses on mammograms in a computer-aided diagnosis scheme.