Quantification of osteoarticular joint defects through bone segmentation and modeling.

Shoulder instability is a major threat to people's daily life. Many patients suffer from shoulder instability such as the loss of the glenoid and humeral head. In clinical practice, an accurate 3D structure estimation of damaged joints is necessary to diagnose and treat bone defects. This study quantifies osteoarticular defects through the modeling and visualization of osteoarticular structures. An improved algorithm to extract the 3D structure of the bones is proposed. The bone contour is then automatically extracted using prior shape and gray scale intensity distribution of joint CT images. Joint structures with mirror symmetry are matched using the Iterative Closest Point registration algorithm. Osteoarticular defects can be quantified on the basis of the symmetric information of the bones. Experimental results demonstrate that the proposed method can effectively segment the joint structures from the CT image. In addition, the proposed mirror symmetrical method can effectively estimate osteoarticular defects.