Fuzzy Clustering with Level Set Segmentation for Detection of Dental Restoration area

In dentistry, dental X-ray images plays an important role in the detection of the different type of abnormalities presents in the teeth. In medical image processing, various image enhancement and segmentation techniques have been used to identify the tooth structures for the classification of the type of abnormalities like tooth fracture, proper root canal treatment, caries identification and periodontal diseases etc. Medical diagnosis can be done manually for dental X-ray images but it is very time consuming and complex. In this paper, we proposed a method to extract the restoration part from the dental X-ray image by combining the Fuzzy clustering with the iterative level set active contour. Here firstly we use the Preprocessing using median filtering to remove the noise present in the X-ray image so that it can be used for further processing. Secondly, we used Fuzzy clustering image segmentation to identify different clusters. At last, Level set active contour method is applied to extract the restoration area from the teeth. The accuracy of the proposed method is more than 98%.