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

Muthu Rama Krishnan Mookiah, Mousumi Pal, S.K. Bomminayuni, Chandan Chakraborty, R.R. Paul, J. Chatterjee, A.K. Ray

Research output: Contribution to journalArticle

31 Citations (Scopus)

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.
Original languageEnglish
Pages (from-to)1096-1104
Number of pages9
JournalComputers in Biology and Medicine
Volume39
Issue number12
DOIs
Publication statusPublished - Dec 2009

Keywords

  • Sub-epithelial connective tissue (SECT)
  • Oral sub-mucous fibrosis (OSF)
  • Multi-level thresholding
  • Support vector machine (SVM)

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