Diagnosis of dental deformities in cephalometry images using support vector machine

This paper proposes an automated target recognition algorithm using Support Vector Machine (SVM) to extract landmark points for craniofacial features in cephalometry radiograph. The features are extracted by subjecting the radiograph to the Projected Principle Edge Distribution (PPED) algorithm. Edge flags are accumulated in every four columns and spatial distribution of edge flags are represented by a histogram. The resultants are the front end of support vector machine. Vectors, which possess land marks, are separated from all other vectors. The centroid points, automatically determined from PPED vectors, are the location of landmarks. The landmark points which are serving as a guide for construction and measurement of planes, are used to evaluate the dento-facial relationship, study of growth and development, and also for treatment planning.