Evaluating the efficiency of infrared breast thermography for early breast cancer risk prediction in asymptomatic population

Abstract

The high incidence and mortality rate of breast cancer in India and the limitations of gold standard method X-ray mammography to be used as a screening and diagnostic modality in young women tempted us to evaluate the efficiency of highly sensitive and non-radiating Infrared Breast Thermography (IBT) in early breast abnormality detection. This study investigates the efficiency of IBT by doing Temperature based analysis (TBA), Intensity based analysis (IBA), and Tumor Location Matching (TLM). In TBA and IBA, several temperature and intensity features were extracted from each thermogram to characterize healthy, benign and malignant breast thermograms. In TLM, the locations of suspicious regions in thermograms were matched with the tumor locations in mammograms/Fine Needle Aspiration Cytology images to prove the efficiency of IBT. Thirteen different sets of features have been created from the extracted temperature and intensity features and their classification performances have been evaluated by using Support Vector Machine with Radial basis function kernel. Among all feature sets, the feature set comprising the statistically significant (p < 0.05) features provides the highest classification accuracy of 83.22% with sensitivity 85.56% and specificity 73.23%. Based on the results of this study, IBT is found to be potential enough to be used as a proactive technique for early breast abnormality detection in asymptomatic population and hence, capable of identifying the subjects that need urgent medical attention.