An Automated Algorithm for Classification and Quantitative Characterization of Breast Cancer by Thermal Imaging

Clinical infrared thermography, a non-contact, non-invasive, non-hazardous technique is accepted as a reliable diagnostic tool for detecting cancer even at the earlier stages of formation. Here, temperature variations are mapped into thermographs. Temperature distribution is uniform and symmetric for normal conditions. On the other hand, an abnormality is indicated by non- uniform and non-symmetric thermal patterns in a thermograph. Abnormality may be due to pain, swelling, Tuberculosis, Fibroadenoma or cancer. This paper proposes an automated technique for classification of cancer regions from fibroadenoma. Also cancer regions are extracted using thresholding and region growing techniques. In this paper, significance of wavelet based smoothing and cascaded wavelet based smoothing techniques in removing the undesirable regions is studied.