A new paradigm of oral cancer detection using digital infrared thermal imaging

M. Chakraborty; S. Mukhopadhyay; A. Dasgupta; S. Banerjee; S. Mukhopadhyay; S. Patsa; J. G. Ray; K. Chaudhuri

doi:10.1117/12.2216785

24 March 2016 A new paradigm of oral cancer detection using digital infrared thermal imaging

M. Chakraborty, S. Mukhopadhyay, A. Dasgupta, S. Banerjee, S. Mukhopadhyay, S. Patsa, J. G. Ray, K. Chaudhuri

Proceedings Volume 9785, Medical Imaging 2016: Computer-Aided Diagnosis; 97853I (2016) https://doi.org/10.1117/12.2216785
Event: SPIE Medical Imaging, 2016, San Diego, California, United States

Abstract

Histopathology is considered the gold standard for oral cancer detection. But a major fraction of patient pop- ulation is incapable of accessing such healthcare facilities due to poverty. Moreover, such analysis may report false negatives when test tissue is not collected from exact cancerous location. The proposed work introduces a pioneering computer aided paradigm of fast, non-invasive and non-ionizing modality for oral cancer detection us- ing Digital Infrared Thermal Imaging (DITI). Due to aberrant metabolic activities in carcinogenic facial regions, heat signatures of patients are different from that of normal subjects. The proposed work utilizes asymmetry of temperature distribution of facial regions as principle cue for cancer detection. Three views of a subject, viz. front, left and right are acquired using long infrared (7:5 - 13μm) camera for analysing distribution of temperature. We study asymmetry of facial temperature distribution between: a) left and right profile faces and b) left and right half of frontal face. Comparison of temperature distribution suggests that patients manifest greater asymmetry compared to normal subjects. For classification, we initially use k-means and fuzzy k-means for unsupervised clustering followed by cluster class prototype assignment based on majority voting. Average classification accuracy of 91:5% and 92:8% are achieved by k-mean and fuzzy k-mean framework for frontal face. The corresponding metrics for profile face are 93:4% and 95%. Combining features of frontal and profile faces, average accuracies are increased to 96:2% and 97:6% respectively for k-means and fuzzy k-means framework.

Citation Download Citation

M. Chakraborty, S. Mukhopadhyay, A. Dasgupta, S. Banerjee, S. Mukhopadhyay, S. Patsa, J. G. Ray, and K. Chaudhuri "A new paradigm of oral cancer detection using digital infrared thermal imaging", Proc. SPIE 9785, Medical Imaging 2016: Computer-Aided Diagnosis, 97853I (24 March 2016); https://doi.org/10.1117/12.2216785

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