An efficient clustering technique and analysis of infrared thermograms

This work proposes an efficient clustering technique for the localization of normal and abnormal tissues using the thermal data obtained from Digital Infrared Thermal Imaging. 10 normal and abnormal raw thermograms are preprocessed and by using K-means clustering, the heat patterns of the thermograms are clustered into various objects using the Euclidean distance metric. Further, breast thermograms are analysed, extracting the region of abnormality by utilizing the fuzzy nature of these thermograms. Features extracted from the simulations conducted on breast thermograms are compared and a distinctive variation is observed. These features can be used efficiently to identify normal and abnormal tissues.