The purpose of this study is to develop an automated scheme of carotid artery calcification (CAC) detection on dental panoramic radiographs (DPRs). The CAC is one of the indices for predicting the risk of arteriosclerosis. First, regions of interest (ROIs) that include carotid arteries are determined on the basis of inflection points of the mandibular contour. Initial CAC candidates are detected by using a grayscale top-hat filter and a simple grayscale thresholding technique. Finally, a rule-based approach and a support vector machine to reduce the number of false positive (FP) findings are applied using features such as area, location, and circularity. A hundred DPRs were used to evaluate the proposed scheme. The sensitivity for the detection of CACs was 90% with 4.3 FPs (80% with 1.9 FPs) per image. Experiments show that our computer-aided detection scheme may be useful to detect CACs.

Measurement of an individual molar provides rich information for forensic personal identification. We propose a computer-based system for extracting an individual molar from dental panoramic radiographs. A molar is obtained by extracting the region-of-interest, separating the maxilla and mandible, and extracting the boundaries between teeth. The proposed system is almost fully automatic; all that the user has to do is clicking three points on the boundary between the maxilla and the mandible.