Automated classification of cells in sub-epithelial connective tissue of oral sub-mucous fibrosis-An SVM based approach

Authors:
M. Muthu Rama Krishnan;Mousumi Pal;Suneel K Bomminayuni;Chandan Chakraborty;Ranjan Rashmi Paul;Jyotirmoy Chatterjee;Ajoy K. Ray
Affiliations:
School of Medical Science & Technology, I.I.T, Kharagpur, India;Department of Oral and Maxillofacial Pathology, Guru Nanak Institute of Dental Science and Research, Panihati, Kolkata, India;Department of Electronics & Electrical Communication Engineering, I.I.T, Kharagpur, India;School of Medical Science & Technology, I.I.T, Kharagpur, India;Department of Oral and Maxillofacial Pathology, Guru Nanak Institute of Dental Science and Research, Panihati, Kolkata, India;School of Medical Science & Technology, I.I.T, Kharagpur, India;School of Medical Science & Technology, I.I.T, Kharagpur, India and Department of Electronics & Electrical Communication Engineering, I.I.T, Kharagpur, India
Venue:
Computers in Biology and Medicine
Year:
2009

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Abstract

Quantitative evaluation of histopathological features is not only vital for precise characterization of any precancerous condition but also crucial in developing automated computer aided diagnostic system. In this study segmentation and classification of sub-epithelial connective tissue (SECT) cells except endothelial cells in oral mucosa of normal and OSF conditions has been reported. Segmentation has been carried out using multi-level thresholding and subsequently the cell population has been classified using support vector machine (SVM) based classifier. Moreover, the geometric features used here have been observed to be statistically significant, which enhance the statistical learning potential and classification accuracy of the classifier. Automated classification of SECT cells characterizes this precancerous condition very precisely in a quantitative manner and unveils the opportunity to understand OSF related changes in cell population having definite geometric properties. The paper presents an automated classification method for understanding the deviation of normal structural profile of oral mucosa during precancerous changes.