Breast cancer detection using MRF-based probable texture feature and decision-level fusion-based classification using HMM on thermography images. (March 2016) - Rastghalam, Rozita; Pourghassem, Hossein
Abstract: Breast cancer is one of the major causes of death for women in the last decade. Thermography is a breast imaging technique that can detect cancerous masses much faster than the conventional mammography technology. In this paper, a breast cancer detection algorithm based on asymmetric analysis as primitive decision and decision-level fusion by using Hidden Markov Model (HMM) is proposed. In this decision structure, by using primitive decisions obtained from extracted features from left and right breasts and also asymmetric analysis, final decision is determined by a new application of HMM. For this purpose, a novel texture feature based on Markov Random Field (MRF) model that is named MRF-based probable texture feature and another texture feature based on a new scheme in Local Binary Pattern (LBP) of the images are extracted. In the MRF-based probable texture feature, we try to capture breast texture information by using proper definition of neighborhood system and clique and also determination of new potential functions. Ultimately, our proposed breast cancer detection algorithm is evaluated on a variety dataset of thermography images and false negative rate of 8.3% and false positive rate of 5% are obtained on test image dataset. Highlights: We propose a two-stage breast cancer detection algorithm by decision-level fusion. We tried to improve false accept of previous algorithms by our proposed algorithm. We used Hidden Markov Model as a fusion algorithm to fuse primitive decisions. We propose a novel texture feature based on Markov Random Field model. To extract color and edge information of images, we modified Local Binary Pattern.