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Osteoarthritis (OA) is the most common form of joint disease and often characterized by cartilage changes. Accurate quantitative methods are needed to rapidly screen large image databases to assess changes in cartilage morphology. We therefore propose a new automatic atlas-based cartilage segmentation method for future automatic OA studies.Atlas-based segmentation methods have been demonstrated to...
Automatic accurate segmentation methods are needed to assess longitudinal cartilage changes in osteoarthritis (OA). We propose a novel general spatio-temporal three-label segmentation method to encourage segmentation consistency across time in longitudinal image data. The segmentation is formulated as a convex optimization problem which allows for the computation of globally optimal solutions. The...
In this paper, we propose a multi-atlas-based method to automatically segment the femoral and tibial cartilage from T1 weighted magnetic resonance (MR) knee images. The segmentation result is a joint decision of the spatial priors from a multi-atlas registration and the local likelihoods within a Bayesian framework. The cartilage likelihoods are obtained from a probabilistic k nearest neighbor classification...
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