Metal Artifact Reduction and Segmentation of Dental Computerized Tomography Images Using Least Square Support Vector Machine and Mean Shift Algorithm

Segmentation and three-dimensional (3D) visualization of teeth in dental computerized tomography (CT) images are of dentists’ requirements for both abnormalities diagnosis and the treatments such as dental implant and orthodontic planning. On the other hand, dental CT image segmentation is a difficult process because of the specific characteristics of the tooth's structure. This paper presents a method for automatic segmentation of dental CT images. We present a multi-step method, which starts with a preprocessing phase to reduce the metal artifact using the least square support vector machine. Integral intensity profile is then applied to detect each tooth's region candidates. Finally, the mean shift algorithm is used to partition the region of each tooth, and all these segmented slices are then applied for 3D visualization of teeth. Examining the performance of our proposed approach, a set of reliable assessment metrics is utilized. We applied the segmentation method on 14 cone-beam CT datasets. Functionality analysis of the proposed method demonstrated precise segmentation results on different sample slices. Accuracy analysis of the proposed method indicates that we can increase the sensitivity, specificity, precision, and accuracy of the segmentation results by 83.24%, 98.35%, 72.77%, and 97.62% and decrease the error rate by 2.34%. The experimental results show that the proposed approach performs well on different types of CT images and has better performance than all existing approaches. Moreover, segmentation results can be more accurate by using the proposed algorithm of metal artifact reduction in the preprocessing phase.