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Abstract

Oral diseases affect people of all ages and are very common worldwide. X-rays are used by dentists to examine the characteristics of oral diseases. Compared to other types of medical images, dental X-ray images present a number of challenges for segmentation and analysis. This makes dental X-beam imaging more testing in view of unfortunate goal, which makes the division of various pieces of teeth and their anomalies temperamental. Dental X-ray Image Segmentation (DXIS) has been demonstrated to be an essential and primary step in obtaining pertinent and significant information about oral diseases. DXIS assumes a significant part in viable dentistry to assist with recognizing different periodontal illnesses. The proposed method helps with further analysis by automatically segmenting the regions of the teeth. It works on dental radiographic images that are both peri-apical and panoramic.The first area of interest is selected using neutrosophic logic. Restricting computation to the foreground regions is the most effective strategy for speeding up the system and improving performance. The patch level feature, the gradient feature, the entropy feature, and the local binary pattern are used to map the dental radiographic image that was input into the neutrosophic domain. By applying neutrosophic logic, the initial area of interest can be pinpointed. After that, a fuzzy c-means algorithm is used to divide up a more precise area of interest. The public data sets "Panoramic Dental X-rays with Segmented Mandibles" and "Digital Dental X-ray Database for Caries Screening" were used to evaluate the proposed method, and the results showed that it was as accurate as 93.20 percent. This exhibition level affirms that the proposed division strategy exceptionally corresponds with the manual framework.