Total variation based edge enhancement for level set segmentation and asymmetry analysis in breast thermograms.

In this work, an attempt has been made to perform asymmetry analysis in breast thermograms using non-linear total variation diffusion filter and reaction diffusion based level set method. Breast images used in this study are obtained from online database of the project PROENG. Initially the images are subjected to total variation (TV) diffusion filter to generate the edge map. Reaction diffusion based level set method is employed to segment the breast tissues using TV edge map as stopping boundary function. Asymmetry analysis is performed on the segmented breast tissues using wavelet based structural texture features. The results show that nonlinear total variation based reaction diffusion level set method could efficiently segment the breast tissues. This method yields high correlation between the segmented output and the ground truth than the conventional level set. Structural texture features extracted from the wavelet coefficients are found to be significant in demarcating normal and abnormal tissues. Hence, it appears that the asymmetry analysis on segmented breast tissues extracted using total variation edge map can be used efficiently to identify the pathological conditions of breast thermograms.