Classification and Recognition of Dental Images Using a Decisional Tree

Recognition and classification of images have a wide field of applications, especially in medical images. In order to provide orthodontists a solution for classification of patients' images to evaluate the evolution of their treatment, we need to use latest efficient technics of classification. In this paper, we propose an algorithm based on a decisional tree to classify and recognize 19 types of dental images. This hierarchical representation can be interpreted as a set of hierarchical types stored in leafs tree structure. By using several extracted features from color images acquired with a digital camera and grayscale images acquired by x-ray scanner. Such as facial features and skin color using YCbCr color-space. The proposed technique has been evaluated on a large data set of four main types namely: mold, intra-oral, extra-oral and radiographic images of different patients. Hence, experimental results demonstrate the good performances of this approach.