Authors: Sourav Pramanik; Debotosh Bhattacharjee; Mita Nasipuri
Addresses: Department of Computer Science and Engineering, Jadavpur University, Kolkata 700032, India ' Department of Computer Science and Engineering, Jadavpur University, Kolkata 700032, India ' Department of Computer Science and Engineering, Jadavpur University, Kolkata 700032, India
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.
Keywords: thermal breast image; texture feature; vascular feature; FBNN; lateral view breast thermogram.
International Journal of Computational Science and Engineering, 2019 Vol.18 No.2, pp.140 - 153
Received: 23 Feb 2017
Accepted: 20 Nov 2017
Published online: 14 Feb 2019 *