Detection of the size of periapical lesions using particle swarm optimization algorithm

Abstract

BACKGROUND: One of the major problems of clinicians in observing the progress of the lesion, is that they have to compare new X-ray radiographs of patients with previous ones to determine the changes of the size of the lesion, and this would be associated with interpretation errors. Using a smart system in detection of the exact size of periapical lesions, we have responded to this problem, in this work. The purpose of this paper is detection of the size of periapical lesions with processing image using particle swarm optimization (PSO) algorithm in the X-Ray Digital (XRD) images that facilitate conducting a more accurate diagnosis. METHODS: Particle swarm optimization, in principle, is a computing evolutionary technique and an optimization population-based method. This algorithm is based on examination of the color changes around the tooth roots in the XRD images. The color of the periapical lesions around unhealthy tooth root is darker (Lucent) compared with that of the healthy tooth root (Opaque). Methodology of this algorithm on XRD image is to investigate the color changes around tooth root and to show the size of periapical lesions. The difference between this study and previous ones is computation of the color changes by image processing algorithm for diagnosis of the size of periapical lesions. RESULTS: After running the algorithm, if the lesion is apical root around, PSO algorithm can recognize size of periapical lesions and identify its location. CONCLUSIONS: This algorithm provides useful and successful results for the presented tests and experiments. Using this algorithm, it is possible to save time, reduce errors, and have a more accurate diagnosis. Among the potential applications of this algorithm is to intelligently help dentist robots, which will be used in the future.