Performance Analysis of Image Segmentation for Oral Tissue

Digital image segmentation is the first step in computer aided diagnostic procedures which are carried out with the help of medical images in the medical field. In this paper, segmentation of Hematoxylin and Eosin (H&E)-stained microscopic image of stratified squamous epithelial layer of oral cavity to separate the squamous cells from the background is performed by different approaches. Due to the complex structure of the squamous epithelial layer, the widely used K-means clustering and thresholding techniques are either not suitable for segmenting such images or unable to furnishthe suitable result. In this work, we are proposing new method for segmentation using Gabor filter. The input image is filtered through a bank of Gabor filters. The number of scales used in constructing the bank of filters is adaptive and automatically computed based on the size of the image. Filtered outputs are taken as 2-dimentional feature vectors. Furthermore, principal component analysis is performed to reduce the dimensionality. In addition, the first principal component is used as the feature image for further processing towards segmentation. This feature image is given as input to both the K-means clustering and thresholding for the final segmentation. The outputs of different approaches are compared. It is found that Gabor filter with thresholding and K-means clustering offers improved result as compared to the conventional ones.