Orchard Boumans Algorithm and MRF Approach Based on Full Threshold Segmentation for Dental X-Ray Images

Abstract

Dental X-ray segmentation uses different image processing (IP) methods helpful in diagnosing medical applications, clinical purposes & in real-time. These methods aim to define the segmentation of various tooth structures in dental X-rays which are utilized to identify caries, tooth fractures, treatment of root canals, periodontal diseases, etc. The manual segmentation of Dental X-ray images for medical diagnosis is very complex and time-consuming from broad clinical databases. Orchard & Bouman is a color quantization approach used to evaluate a successful cluster division using an eigenvector of a color covariance matrix. It is repeated until the number of target clusters is reached. It is optimal for large clusters with Gaussian distributions to integrate different types of information on probabilism and spatial constraint by iteratively upgrading the later probability of the proposed model. Results of segmentation are achieved when iteration converges. Testing the proposed model's effectiveness will involve texture, distance sensing, and nature images. Experimental results show that our model achieves a higher segmentation precision with approximately 78.98 PSNR than MRF models based on pixels or regions.