title: Pre-cancer risk assessment in habitual smokers from DIC images of oral exfoliative cells using active contour and SVM analysis.
abstract: Habitual smokers are known to be at higher risk for developing oral cancer, which is increasing at an alarming rate globally. Conventionally, oral cancer is associated with high mortality rates, although recent reports show the improved survival outcomes by early diagnosis of disease. An effective prediction system which will enable to identify the probability of cancer development amongst the habitual smokers, is thus expected to benefit sizable number of populations. Present work describes a non-invasive, integrated method for early detection of cellular abnormalities based on analysis of different cyto-morphological features of exfoliative oral epithelial cells. Differential interference contrast (DIC) microscopy provides a potential optical tool as this mode provides a pseudo three dimensional (3-D) image with detailed morphological and textural features obtained from noninvasive, label free epithelial cells. For segmentation of DIC images, gradient vector flow snake model active contour process has been adopted. To evaluate cellular abnormalities amongst habitual smokers, the selected morphological and textural features of epithelial cells are compared with the non-smoker (-ve control group) group and clinically diagnosed pre-cancer patients (+ve control group) using support vector machine (SVM) classifier. Accuracy of the developed SVM based classification has been found to be 86% with 80% sensitivity and 89% specificity in classifying the features from the volunteers having smoking habit.
authors.affiliation: Centre for Health Care Science and Technology, IIEST, Shibpur, India; B.P. Poddar Institute of Management & Technology, 137, V.I.P. Road, Kolkata, India.
authors.forename: Susmita
authors.initials: S
authors.lastname: Dey
authors.affiliation: Centre for Health Care Science and Technology, IIEST, Shibpur, India.
authors.forename: Ripon
authors.initials: R
authors.lastname: Sarkar
authors.affiliation: Department of Oral and Maxillofacial Pathology, Buddha Institute of Dental Sciences, Patna, India.
authors.forename: Kabita
authors.initials: K
authors.lastname: Chatterjee
authors.affiliation: Centre for Health Care Science and Technology, IIEST, Shibpur, India.
authors.forename: Pallab
authors.initials: P
authors.lastname: Datta
authors.affiliation: Centre for Health Care Science and Technology, IIEST, Shibpur, India. Electronic address: ananya.pariksha@gmail.com.
authors.forename: Ananya
authors.initials: A
authors.lastname: Barui
authors.affiliation: Department of Information Technology, IIEST, Shibpur, India.
authors.forename: Santi P
authors.initials: SP
authors.lastname: Maity
mesh_terms: D055088:Early Detection of Cancer; D004847:Epithelial Cells / Q000473:pathology / Q000648:ultrastructure*; D005260:Female; D006801:Humans; D007091:Image Processing, Computer-Assisted; D008297:Male; D008857:Microscopy, Interference; D009061:Mouth Mucosa / Q000473:pathology / Q000648:ultrastructure; D009062:Mouth Neoplasms / Q000175:diagnosis* / Q000473:pathology; D012907:Smoking / Q000009:adverse effects*; D060388:Support Vector Machine
keywords: DIC image; Exfoliative cytology; Non-invasive detection; Oral cancer; SVM classifier