Analysis of Breast Thermograms Using Asymmetry in Infra-Mammary Curves.

The objective of this research is to propose a methodology to analyse breast thermograms in order to detect breast abnormalities, including cancer. This research work mainly target to segmented ROI that show significant increase in temperature as compared to the neighbouring areas and contralateral sides in breast thermograms. The captured frontal thermograms from each patient is initially smoothed using a Gaussian filter with a standard deviation σ = 1.4 to reduce noise. Region of interest is segmented using bifurcation points obtained by identifying curve that passes through infra-mammary fold. Infra-mammary curve is detected using Horizontal projection profile. Once the segmentation for analysis is determined, exact location of an abnormality or a lesion is determined. Heat patterns are analysed for symmetry. Asymmetry analysis usually helps to detect abnormalities. Significance and challenges of thermal images are discussed. Once the segmentation for analysis is determined, exact location of an abnormality or a lesion is determined. Heat patterns are analysed for symmetry. Asymmetry analysis usually helps to detect abnormalities. Further, classifiers based on support vector machine and principal component analysis were tested on the dataset used for evaluation. Experimental results and statistical analysis support the proposed methodology is able to detect breast anomalies with higher accuracy. An average accuracy of 95%, sensitivity of 97.05% and specificity of 92.3% was obtained for a set of sixty images with 35 normal and 25 abnormal thermograms using SVM-RBF classifier.