DISR: Dental Image Segmentation and Retrieval

Abstract

In this paper we propose novel algorithms for retrieving dental images from databases by their contents. Based on special information of dental images, for better content based dental image retrieval and representation, the image attributes are used. We propose DISR (Dental Image Segmentation and Retrieval), a content-based image retrieval method which is robust to translation and scaling of the objects in the images. A novel model is used to calculate the features of the image. We implemented the dentition plaster casts and proposed a special technique for segmenting teeth in our dental study models. For testing the efficiency of the presented algorithm a software system is developed, and 60 dental study models are used. The models are covering different kinds of malocclusions. Our experiments show that 95% of extracted results are accurate and the presented algorithm is efficient.