Breast abnormality detection using combined texture and vascular features. (2019) - Pramanik, Sourav; Bhattacharjee, Debotosh; Nasipuri, Mita
This work presents an integrated approach that combines texture and vascular features for distinguishing malignancy and benignity of breast abnormalities using thermal breast image. A local texture descriptor, called block variance (BV), is used here to extract the texture features. On the other hand, thermo-vascular pattern based features are identified by using a series of morphological operations. Then, these two feature sets are fused together to make a final feature vector. In this work, a five-layer feed forward, back propagation neural network (FBNN) is implemented as a classifier. The breast thermograms of DMR-IR database are used for the purpose of evaluation of the proposed system performance. Experimental results have shown that the proposed method detected malignant cases with 94% accuracy, while benign cases are detected with 100% accuracy. The overall system accuracy is obtained as 97.2%, which is comparatively better than other existing state-of-the-art methods.