Automated classification of cells in sub-epithelial connective tissue of oral sub-mucous fibrosis-an SVM based approach.
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.
Muthu Rama Krishnan
M
M
I.I.T, Kharagpur, India.
Pal
Mousumi
M
Bomminayuni
Suneel K
SK
Chakraborty
Chandan
C
Paul
Ranjan Rashmi
RR
Chatterjee
Jyotirmoy
J
Ray
Ajoy K
AK
D000465:Algorithms; D001185:Artificial Intelligence; D003238:Connective Tissue / Q000473:pathology*; D003936:Diagnosis, Computer-Assisted / Q000379:methods* / Q000706:statistics & numerical data; D003951:Diagnostic Errors; D006801:Humans; D007091:Image Processing, Computer-Assisted / Q000706:statistics & numerical data; D009061:Mouth Mucosa / Q000473:pathology; D009062:Mouth Neoplasms / Q000145:classification / Q000175:diagnosis / Q000473:pathology; D009914:Oral Submucous Fibrosis / Q000145:classification* / Q000175:diagnosis / Q000473:pathology*; D011230:Precancerous Conditions / Q000145:classification / Q000175:diagnosis / Q000473:pathology