Application of Improved PSO in Augmented Reality for Dental Healthcare

Abstract

Augmented Reality (AR) is a unique technological innovation that seamlessly blends virtual and real worlds. Regarding dental healthcare, AR can be used for enhancing the visualization of dental structures, and treatment planning. AR overlays digital information onto the real-world dental environment, allowing dentists to see virtual dental models, treatment simulation on a patient's teeth in real-time. The feature extraction process based on geometric features is the most crucial step in oral image registration. In the presented study, Particle Swarm Optimization algorithm (PSO) is advanced in order to increase the accuracy of the extracted features descriptor by adding Anti-aliasing technique and utilizing the Hamming distance metric to enhancing the performance of PSO-based feature extraction in AR for dental healthcare. Firstly, applying anti-aliasing techniques to the virtual dental models and AR visualization, the edges of the virtual objects can be softened and blended more smoothly with the real dental structures. This, in turn, enhances the reliability of the feature extraction process by providing clearer and more accurate data points for subsequent analysis and treatment planning. Secondly, the Hamming distance metric is utilized to quantify the similarity among various feature vectors derived from dental images, this metric optimizes the feature extraction process in PSO, leading to more accurate and reliable dental health care applications.