抄録
Dental radiographs are of immense help in the identification, and evaluation of oral pathologies. One of the common oral pathology is a dental cyst. Objective quantification of severity and early stage detection highly benefits the diagnosis and treatment of the dental cysts. In this paper, we propose a novel neural network based automated system to identify and quantify the severity of the cysts using dental radiograph images. An automated diagnosis of dental cyst in radiography images based on segmentation algorithm and Artificial Neural Networks (ANN) is presented. By using template-matching approach templates pertaining to various cysts are slide over the input image to obtain the Normalized Cross Correlation (NCC) and Extended Normalized Cross Correlation (ENCC) images. An ANN is trained using ENCC values to locate the suspicious region. The diagnosis of the cysts is brought out by the extraction of connected components in the original image. The results obtained provide details about severity of the cysts and thereby increases the diagnostic ease of dental surgeon.
