Jaw Segmentation from CBCT Images

Abstract

Nowadays, more people pay attention to the dental health including oral cavities, bone tumors or cancers, so the dental CBCT images becomes popular and are widely used in dental diagnosis. Dental implants, orthodontic orthodontics and other surgical procedures are employed in daily life. Accurate jaw separation from neighboring tissues can greatly improve diagnosis results, space measurements and success rates of surgical operations. This paper proposes an automatic segmentation algorithm to separate jaw bone from CBCT images. This algorithm uses the idea of three-dimensional region growing to perform segmentation, then optimizes the segmentation results with active contours. This algorithm yields more accurate segmentation of the jaw bone. Experiments are performed to both manually and automatically segment 10 groups of CBCT datasets. With manual segmentation references, our algorithm demonstrated our automatic segmentation algorithm work well, and further confirmed by evaluation of four quantitative metrics PSNR, SSIM, Precision and Recall. It can potentially assist doctors in diagnosis and surgical planning.