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

•Regular breast health examination is the only way to reduce the breast cancer mortality rate.•Infrared Breast Thermography can pinpoint the minute temperature changes related to breast health.•The non-radiating Infrared breast thermography can be used as a routine breast health checkup tool.•By ide...

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Bibliographic Details
Published in:Infrared physics & technology 2019-06, Vol.99, p.201-211
Main Authors: Gogoi, Usha Rani, Majumdar, Gautam, Bhowmik, Mrinal Kanti, Ghosh, Anjan Kumar
Format: Article
Language:English
Subjects:
Asymptomatic Patients
Breast cancer
Early breast abnormality prediction
Infrared breast thermography
Routine check-up tool
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Summary:•Regular breast health examination is the only way to reduce the breast cancer mortality rate.•Infrared Breast Thermography can pinpoint the minute temperature changes related to breast health.•The non-radiating Infrared breast thermography can be used as a routine breast health checkup tool.•By identifying the breast abnormalities in early stages, infrared breast thermography provides more treatment options. 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 
ISSN:1350-4495
1879-0275
DOI:10.1016/j.infrared.2019.01.004