Soft-tissue segmentation has always been difficult point in the medical research of diagnosis of soft-tissue defects. Especially for Anterior Cruciate Ligament (ACL) rebuilding surgery, ACL segmentation from all soft-tissue inside knee joint, including Posterior Cruciate Ligament (PCL) and meniscus, is a very important task. In this paper, we propose a novel ACL segmentation method: Space Model Contrast Clustering-based (SMC-based) ACL Segmentation. Unlike the widely used processing method, such as segmentation by MRI gray values and Mimics segmentation drawing, the proposed method relies 3D model of knee joint to segment soft tissue by self-adaptive K-means clustering. Extensional experiments demonstrate that the proposed method can be capable of solving the problem of soft-tissue segmentation well and has achieved higher ACL segmentation efficiency.
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