Breast cancer detection using spectral probable feature on thermography images

Thermography is a noninvasive, non-radiating, fast, and painless imaging technique that is able to detect breast tumors much earlier than the traditional mammography methods. In this paper, a novel breast cancer detection algorithm based on spectral probable features is proposed to separate healthy and pathological cases during breast cancer screening. Gray level co-occurrence matrix is made from image spectrum to obtain spectral co-occurrence feature. However, this feature is not sufficient separately. To extract directional and probable features from image spectrum, this matrix is optimized and defined as a feature vector. By asymmetry analysis, left and right breast feature vectors are compared in which certainly, more similarity in these two vectors implies healthy breasts. Our method is implemented on various breast thermograms that are generated by different thermography centers. Our algorithm is evaluated on different similarity measures such as Euclidean distance, correlation and chi-square. The obtained results show effectiveness of our proposed algorithm.