Objectives: The aim of this study was to design and evaluate a new method for automated localization of the inferior alveolar nerve canal on CBCT images. Methods: The proposed method is based on traversing both panoramic and cross-sectional slices. For the panoramic slices, morphological skeletonization is imposed, and a modified Hough transform is used while traversing the cross-sectional slices. A total of 40 CBCT images were randomly selected. Two experts twice located the inferior alveolar nerve canal during two examinations set 6 weeks apart. Agreement between experts was achieved, and the result of this manual technique was considered the gold standard for our study. The distances for the automated method and those determined using the gold standard method were calculated and recorded. The mean time required for the automated detection was also recorded. Results: The average mean distance error from the baseline was 0.75 ± 0.34 mm. In all, 86% of the detected points had a mean error of ,1 mm compared with those determined by the manual gold standard method. Conclusions: The proposed method is far more accurate and faster than previous methods. It also provides more accuracy than human annotation within a shorter time.