Automatic dental CT image segmentation using mean shift algorithm

Abstract

Identifying the structure and arrangement of the teeth is one of the dentists' requirements for performing various procedures such as diagnosing abnormalities, dental implant and orthodontic planning. In this regard, robust segmentation of dental Computerized Tomography (CT) images is required. However, dental CT images present some major challenges for the segmentation that make it difficult process. In this research, we propose a multi-step approach for automatic segmentation of the teeth in dental CT images. The main steps of this method are presented as follows: 1-Primary segmentation to classify bony tissues from nonbony tissues. 2-Separating the general region of the teeth structure from the other bony structures and arc curve fitting in the region. 3-Individual tooth region detection. 4-Final segmentation using mean shift algorithm by defining a new feature space. The proposed algorithm has been applied to several Cone Beam Computed Tomography (CBCT) data sets and quality assessment metrics are used to evaluate the performance of the algorithm. The evaluation indicates that the accuracy of proposed method is more than 97 percent. Moreover, we compared the proposed method with thresholding, watershed, level set and active contour methods and our method shows an improvement in compare with other techniques.