Performance Analysis of Semi Supervised Approach in Dental Field: Survey

Dental X-ray image segmentation (DXIS) is an necessary process in Practical dentistry for diagnosis of periodontitis diseases from an X ray image. Thus, DXIS is one of the most important and necessary steps to analyse dental images. It helps to get worthy information for medical diagnosis support systems and other recognition tools. DXIS helps to get high accuracy of segmentation. In this paper, a new cooperative scheme was proposed that applies semisupervised Fuzzy clustering algorithm to DXIS. Specifically, the Otsu method is used to remove the Background area from an X-ray dental image. Otsu stands for Oral Tracheal Stylet Unit. Thus, Otsu method is used to reduce gray level image into binary image. The FCM algorithm is chosen to remove the Dental Structure area from the results of the previous steps. Fuzzy Clustering Algorithm helps to reveal the underlying structure of the data based on the similarity measure. Atlast, Semi-supervised Entropy regularized Fuzzy Clustering algorithm (eSFCM) is opted to clarify and improve the results and to improve the clustering performance. The proposed framework is evaluated on a real collection of dental X-ray image dataset. The usefulness and significance of this research are fully demonstrated within the extent of real-life application.