Neutrosophic SetBased Caries Lesion Detection Method to Avoid Perception Error Dental caries is an infectious oral disease. The monitoring of caries region boundary, in regular intervals, is important for treatment purpose. To detect dental caries lesion, most of the time dentists use Xray images. Due to human brain perception, sometimes it is difficult to detect the caries lesion accurately by observing the Xray image manually. In this work, a framework has been proposed to detect caries lesion automatically within the optimum time. Almost all caries detection methods from the radiographic images apply iterative methods upon the entire image to separate initially suspected regions. Then, further processing is performed on the separated regions. This method reduced huge computation efforts by avoiding applying iterative methods upon the entire input image. This method transforms the input Xray image into its equivalent neutrosophic domain to obtain initially suspected region. We have used a custom feature named xe2x80x98weightxe2x80x99 for neutrosophication. This feature is calculated by fusing other features differently. Once the initial region is detected, it is examined further to test whether there exists any isocenter rings like the catchment basin. This is the most important property of caries lesions. After the suspected region is confirmed as caries lesion, then the caries boundary is detected using active contour technique. The advantage of this system is that it avoids repetitive iterations at the time of suspected region selection using neutrosophication. Repetitive iterations upon the entire picture dimension are a timeconsuming job. The performance of the proposed research work is satisfactory; the average accuracy is above 92%.

Automatic target segmentation in color dental images Automatic target segmentation is critical to computerized dental imaging systems, which are designed to reduce human effort and error. We have developed an automatic algorithm that is capable of outlining an intraoral reference bar and the tooth of interest. In particular, the algorithm first locates the reference bar using unique color and shape cues. The located reference bar provides an estimate for the tooth of interest in terms of both its scale and location. Next, the estimate is used to initialize a trained active shape model (ASM) consisting of the bar and the tooth. Finally, a search process is performed to find a match between the ASM and the local image structures. Experimental results have shown that our fully automatic algorithm provides accurate segmentation of both the reference bar and the tooth of interest, and it is insensitive to lighting, tooth color, and toothshape variations.