Heuristically Modified Fusion-based Hybrid Algorithm for Enhanced Dental Caries Segmentation

Segmentation is a primarystage in any image applications. The accuracy of the segmentation method establishes the achievement or failure of the final analysis process. In bio medics, Artificial Intelligence (AI) methods have a lifelong impact and attain better results. The purpose of this paper is to estimatethe exactrecognition of caries by feature extraction and classification of the dental images by amalgamation. The research work highlightsthe efficiency of intelligent algorithms for the recognition of dental cavities. The main intention of this paper is to improve a novelfusion model-based dental caries segmentation model that is useful for timely identification and treatment. The suggested model undergoes three phases, namely pre-processing, caries segmentation, and post-processing. The images are pre-processed by the contrast enhancement, bilateral filtering, and Otsu thresholding. Then, the Heuristically Modified Fusion-based Fuzzy C Means (FCM) clustering and Binary Thresolding (HMF -FCBT)is developed for caries segmentation. Here, the Coyote Optimization Algorithm (COA) algorithm is employed for optimizing the criteria such as cluster centroid and iteration of FCM, threshold, and fusion weight, thus improving the segmentation performance. Finally, the morphological operation is performed as the post-processing of segmentation. Thus, the suggested segmentation has performed well in comparison with other conventional approaches.