Automated characterization of sub-epithelial connective tissue cells of normal oral mucosa: Bayesian approach

The objective of this paper is to develop an automated cell classification system based on Bayesian classifier followed by segmentation using color deconvolution and feature extraction for characterizing various types of sub-epithelial connective tissue (SECT) cells from histological images. In the histological sections of oral mucosa, SECT layer mainly consists of three types of cells - inflammatory, fibroblast and endothelial cells; out of which only first two play significant role pertaining to precancerous changes in oral mucosa. In order to discriminate inflammatory and fibroblast cells, a set of mathematical features viz., area, perimeter, eccentricity, compactness, Zernike moments and Fourier descriptors are extracted followed by cell segmentation using color deconvolution method. The features are statiatically analysed to show its significance in cell discrimination. Thereafter, Bayesian classifier is implemented based on the defined feature space for characterizing inflammatory and fibroblast cells in order to observe the cell distribution in healthy state. The performance of this proposed system is evaluated with 97.19% overall classification accuracy.