Semi-supervised OTSU based hyperbolic tangent Gaussian kernel fuzzy C-mean clustering for dental radiographs segmentation

Abstract

Dental periapical X-ray image (DXRI) segmentation is an important process to examine dental images towards diagnosing medical systems is an essential operation within practical dentistry for periodontitis recognition. However, traditional clustering algorithms in image processing frequently accept deficiencies in finding teeth sample boundaries and parameters. The presentation related to clustering is improved while further data produced with the user. In DXRI segmentation, semi supervised fuzzy clustering which is a new collective scheme. Initially, pre-processing is done for the input X-Ray image in order to minimize error. Specifically, Otsu’s method divide the dental X-Ray image into background and foreground regions. Here, the chosen FCM to separate the teeth regions commencing on the preceding steps. A Semi-supervised Hyperbolic Tangent Gaussian kernel Fuzzy C-Means algorithm (HTGkFCM) is preferred so as to increase an outcome that is optimum than compared to the traditional methods. So, current method is less sensitive to noise with robustness. Real datasets for the implementation on the proposed framework with cluster validity computation such as, Davies–Bouldin (DB), Segmentation Accuracy (SA), Simplified Silhouete Width Criterion (SSWC), processing time, PBM and Mean Absolute Error (MAE). So the proposed work has been enhanced in terms of 7%, 2%, 5%, 6%, 3% and 30% than the state-of-the-art works. Simulation outcome shows the quality of clustering in the framework have higher accuracy and more reliable than other clustering methods.