Proposal of index to estimate breast similarities in thermograms using fuzzy C means and anisotropic diffusion filter based fuzzy C means clustering

Abstract Clustering analysis of medical infrared images is found to be very effective for extracting local information, which helps to identify the abnormalities associated with region of interest. Breast tissue extraction in breast thermograms is challenging due to its low signal-to-noise ratio, low contrast and absence of clear edges. Accurate detection of asymmetrical thermal patterns in breast cancer screening is essential for better diagnosis. Therefore, in this work, the limitations are addressed using Anisotropic Diffusion filter based Fuzzy-C-Means clustering (ADFCM). This technique is employed for precise segmentation of hottest regions in breast tissues. Initially, the ground truth masks are multiplied with raw images for the extraction of non-breast tissues. The corresponding left and right breasts are delineated. Anisotropic diffusion filtering is performed on delineated breast tissues and simple thresholding edge map output is integrated with FCM clustering. For comparison, conventional FCM clustering is employed to segment hottest region. The grey level values of hottest regions are extracted by multiplying the clustered output with input image. The mean area of hottest region gray level values is computed for normal and abnormal images. The performance comparison of FCM and ADFCM has been carried out qualitatively and quantitatively. The results show that ADFCM performs very well compared to FCM method. Further, it is observed that asymmetry analysis of breast tissues using ADFCM based clustering is found to be statistically highly significant (ρ