ABSTRACT
Oral Cancer, also called Oral Squamous Cell Carcinoma [OSCC], has been one of the serious cancers that affect the South Asian countries. A range of diagnostic strategies are available including biopsy of the affected part. The Wnt/beta-catenin pathway plays important roles in morphogenesis, normal physiological functions, and tumor formation. This study examined the accumulation of beta-catenin in the nuclei and cytoplasm of oral cancer. The accuracy of histopathological results is hampered by considerable inter and intra-reader variability even by expert pathologists. In order to get both qualitative and quantitative results, we developed a system for diagnosis of oral cancer using Expectatione Maximization [EM algorithm]. The microscopic images of immunohistochemical staining of beta-catenin expression were segmented using Iterative Method of [EM] algorithm to extract the cellular and extracellular components of an image. The segmentation process of the system uses unitone conversion to obtain a single channel image using Principal Component Analysis [PCA] with the highest contrast. Finally, the unitone image is normalized to [0-1] range. Based on the segmentation process we conclude that beta-catenin expression using EM algorithm is an efficient technique to help the pathologist to evaluate the histological changes on microscopic images of oral cancer