Trabecular bone density below the root tip is often used as a basis for determining treatment efficacy. Before comparing the trabeculae at different time points, it is necessary to register longitudinal images to identify the same location of before and after treatment. In order to achieve accurate image registration, it is important to identify representative feature points in the image. Current feature point detection algorithms are unable to detect representative feature points in periapical X-ray images. The algorithm proposed in this study is based on the conventional Barnard feature point detection algorithm to solve the problem regarding excessive quantity and aggregation of feature points for periapical X-ray images with poor contrast. The results of this study showed that the proposed algorithm can successfully identify representative feature points that are uniformly distributed in periapical X-ray images. This algorithm can be used in the future for longitudinal periapical X-ray image registration and to assist dentists in finding the same location of treatment to determine the efficacy of endodontic treatment.