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9 May 2002 Segmentation, surface extraction, and thickness computation of articular cartilage
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Accurate computation of the thickness of articular cartilage in 3D is crucial in diagnosis of joint diseases. The purpose of this research project is to develop an unsupervised method to produce three-dimensional (3D) thickness map of articular cartilage with magnetic resonance imaging (MRI). The method consists of two main parts, cartilage extraction and thickness map computation. The initial segmentation for cartilage extraction is achieved using a recently proposed algorithm which depends on region-growing. The regions produced during this process are labeled as cartilage or non-cartilage using a voting procedure which essentially depends on local 2-class clustering and makes use of prior knowledge about cartilage regions. Following cartilage extraction, femoral and tibial cartilages are separated by detecting the interface between them using a deformable model. After the separation, the cartilage surfaces are reconstructed as a triangular mesh and divided into two plates according to the relation between surface normal at each vertex and principal axes of the structure. For surface reconstruction, we propose an algorithm which incorporates a simple MR imaging model which allows surface representations with sub-voxel accuracy. Our thickness computation algorithm treats each plate separately as a deformable model while considering the other plate as the target surface towards which it is deformed. At the end of deformation, the thickness values at each vertex is defined as the distance between the locations at pre and post-deformation instances. The performance of the cartilage segmentation is compared to manual tracing. Also, the performance evaluation of the thickness computation algorithm on phantoms resulted in RMS errors on the order of 1%.
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S. Kubilay Pakin, Jose Gerardo Tamez-Pena, Saara Totterman, and Kevin J. Parker "Segmentation, surface extraction, and thickness computation of articular cartilage", Proc. SPIE 4684, Medical Imaging 2002: Image Processing, (9 May 2002);

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