• M.Vimala
• Dr.P.Ranjith Kumar

Dental X-ray image segmentation (DXIS) is a crucial process in dentistry for diagnosis the diseases from an X-ray image. DXIS used specialized data mining method to achieve higher accuracy of segmentation. Clustering algorithm is used to determine the common boundaries of teeth samples. In this paper, we propose a new cooperative scheme that applies semi-supervised adaptive fuzzy clustering algorithms. Specifically, the Otsu method is used to remove the Background area from an X-ray dental image. Then, the FCM algorithm is chosen to remove the Dental Structure area from the results of the previous steps. Semi-supervised Entropy regularized Fuzzy Clustering algorithm (eSFCM) is adapted for clarifying and improve the results based on the optimal result from the previous clustering method. It solves the model by Interactive model by using cluster centers and membership matrix. The feature is extracted using the method of Linear Binary Pattern (LBP). In the view of expert systems, this method made use of knowledge-based algorithms for a practical application. The achieved results are then rectified by mean of eSFCM with a pre-defined membership matrix being taken from the FCM reduction by the maximum operating. A new semi-supervised adaptive fuzzy clustering algorithm segments (SSAFCM) a dental X-Ray image. The adaptive FCM allows pixels that belong to numerous clusters with changeable degrees of membership. It may able to determine final segmentation results from the pattern in a reasonable processing manner. The various schemes are (i) SSAFC-LBP with Otsu have better performance than the relevant methods such as Fuzzy C-Means, Otsu, and other semi-supervised fuzzy clustering algorithms namely eSFCM and SSFCLBP for dental X-ray image segmentation problem. The usefulness and significance of this research are clearly demonstrated within the extent of real-life applications.

Dental Image Segmentation, Fuzzy Clustering, Semi supervised Entropy regularized Fuzzy Clustering Algorithm, Semi Supervised Adaptive Fuzzy Clustering.

"STUDY OF SEMI-SUPERVISED ADAPTIVE FUZZY CLUSTERING FOR IMAGE SEGMENTATION", International Journal of Emerging Technologies and Innovative Research (www.jetir.org), ISSN:2349-5162, Vol.6, Issue 2, page no.153-159, February-2019, Available :http://www.jetir.org/papers/JETIR1902418.pdf

"STUDY OF SEMI-SUPERVISED ADAPTIVE FUZZY CLUSTERING FOR IMAGE SEGMENTATION", International Journal of Emerging Technologies and Innovative Research (www.jetir.org | UGC and issn Approved), ISSN:2349-5162, Vol.6, Issue 2, page no. pp153-159, February-2019, Available at : http://www.jetir.org/papers/JETIR1902418.pdf