An effective teeth segmentation method for dental periapical radiographs based on local singularity

Abstract

Dental radiographs play an important role for dental diagnosis, as most anomalies are hidden under the surface and cannot be seen during a visual examination. For effective computer-aided dental diagnosis, accurate teeth segmentation is one of the most critical tasks, because cysts and inflammatory lesions usually occur around tooth periapical (tooth-roots) areas and these areas in radiographs are often subject to noise, low contrast, and uneven illumination. In this paper, we propose an effective scheme to segment each tooth in dental periapical radiographs based on local singularity analysis. At first, a proposed adaptive power law transformation is applied to reduce variations of contrasts between teeth and alveolar bones (gums). Then local singularities measured by Hölder exponent are computed to obtain a structure image in which the structures of teeth are much smoother than the structures of gums. Otsu's thresholding is applied to segment teeth from gums and finally, connected component analysis and morphological operations are applied to isolate each tooth. Experimental results demonstrate that out of 18 teeth in six tested periapical images, all teeth are successfully segmented with 17 extracted tooth-contours almost completely conforming to human visual perception.