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A new approach of oral cancer detection using bilateral texture features in digital infrared thermal images

Oral cancer is one of the most prevalent form of cancer and its severity is aggrandized specially among the socio-economically backward population in developing countries. A major fraction of patient population is unable to avail diagnosis for oral cancer due to scarcity of state-of-the-art infrastructure and experienced oral and maxillofacial pathologist. Contemporary gold standard of oral cancer confirmation relies on biopsy report. But biopsy is invasive and thus patients are usually reluctant to undergo this test. Moreover, biopsy yields considerable false negatives if investigated tissue is not collected precisely from the carcinogenic location. Till date, there is dearth of computer aided pre-screening tool for detection of oral cancer. The paper presents Digital Infrared Thermal Imaging as a viable modality for early screening of oral cancer. This is the pioneering attempt to discriminate normal subjects from patients by leveraging discriminating texture features on oral thermograms. Statistically significant texture features were selected from a) both halves of frontal face and b) right and left profile faces. Due to disparity of distribution of facial temperature between normal subjects and patients, the corresponding texture features form discriminative class specific local clusters. Such local conglomeration was exploited using k-means and fuzzy k-means clustering. We adopt the concept of cluster prototype classifier which assigns label to each cluster according to majority class labels within that cluster. Highest classification accuracy of 86.12% is attained on fusion of features from left and right half of frontal face of precancerous subject followed by fuzzy k-means guided cluster prototype classification. The proposed work outperforms our previously developed pre-screening framework by upto 6.5%. Such promising results boosts the viability of our approach.