CURRENT AUTOMATIC METHODS FOR KNEE CARTILAGE SEGMENTATION: A REVIEW

Knee cartilage segmentation has been challenging task for many years. This task is usually connected with two major issues. First object of interest is automatic detection and extraction of knee cartilage shape. Second important issue is detection of osteoarthritis, especially, in early stages. This early deterioration is badly recognizable from native images segmentation significantly contributes to precise localization, detection and extraction of early osteoarthritis. Generally, the knee cartilage automatic segmentation and extraction can be performed by various approaches including edge tracking, intensity-based methods, supervised learning, energy minimization, statistical methods and multiregional segmentation methods. Using of particular segmentation method depends on a compromise which user is willing to accept with respect to robustness, segmentation purpose, computational time, accuracy and level of user interaction. This review is mainly focused on fully automatic segmentation methods bringing the recent informations about modeling of cartilage structure via segmentation approaches.