Texture analysis of breast thermograms using neighbourhood grey tone difference matrix

Abstract

Breast cancer is the leading cancer in women worldwide. Early detection can reduce the mortality rate of breast cancer. Breast thermography is a non-invasive and simple imaging technique used for early detection of breast cancer. Feature extraction and selection of appropriate features play a major role in computer-aided detection of breast cancer using breast thermograms. In this article, texture features are extracted from automatically segmented breast thermograms by computing neighbourhood grey tone difference matrix (NGTDM) and run length matrix (RLM). Significance of these features in differentiating the abnormal breast from the normal breast is found by statistical test. NGTDM extracted coarseness, busyness, complexity, strength and RLM extracted long run emphasis and run percentage are found to be significant by statistical test. Extracted features are computationally less expensive and attained an average accuracy of 80%, sensitivity of 94% and specificity of 71.4% using back propagation neural network classifier.