Automatic Image Processing Based Dental Image Analysis Using Automatic Gaussian Fitting Energy and Level Sets

Abstract

Identification of the Root canal length is a major concern in the dentistry worldwide, which currently seeks the manual calculation in order to detect the measurement of the teeth. Intensity inhomogeneity often is a major problem in dental x-rays which causes considerable difficulties in segmentation. For better computer-aided diagnosis in dentistry, having a precise tooth segmentation is a critical task, as the cysts and inflammatory lesions generally occur around tooth root areas and these areas in radiographs are generally subject to noise, poor contrast, and very uneven illumination. This paper presents an effective segmentation method using a combinational approach of Local Gaussian Distribution fitting energy along with level sets. Here the local intensities of images are defined by Gaussian distributions which are combined with the level set function for accurate segmentations of teeth contour. The experimental results indicate that segmentation achieves the less number of iterations making it computationally fast and work in real time situation.