Detecting and visualizing cartilage thickness without a shape model

Abstract

This paper proposes a cartilage thickness detection and visualization method that does not utilize a shape model. The proposed method consists of three parts: volume of interest (VOI) initialization, bone segmentation, and cartilage thickness visualization. For VOI initialization, a novel 3D U-shape cuboidal filter is proposed to detect individual bones such as the femur, tibia, and patella, and for bone segmentation, a hybrid level-set method is adopted. Finally, a surface normal based approach is presented for measuring and visualizing the cartilage thickness. The advantage of the proposed method compared to other methods is that it does not require a shape model or any training process. The results demonstrate that the proposed method can be used for inspecting cartilage damage and loss.