Breast abnormality detection using combined texture and vascular features. (2019)

• Record Type: Journal Article
• Authors: Pramanik, Sourav; Bhattacharjee, Debotosh; Nasipuri, Mita
• Is Part Of: International journal of computational science and engineering. Volume 18:Number 2(2019)
• Page Start: 140
• Page End: 153
• Publication Date: 2019
• Subjects: thermal breast image -- texture feature -- vascular feature -- FBNN -- lateral view breast thermogram; Computer science -- Mathematics -- Periodicals; Computer simulation -- Mathematical aspects -- Periodicals; Computational intelligence -- Periodicals; 004.015105
• Languages: English
• ISSNs: 1742-7185

Abstract

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.