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.Efficient, reproducible segmentation of cartilage tissue in MR images.
Chondral damage resulting from osteoarthritis, septic arthritis, or trauma of the knee joint is typically characterized by changes in the morphology of the cartilage tissue of the joint. Especially, for accurate diagnosis and follow-up of these pathologies, such morphological changes of the cartilage tissue have to be quantified. The most common method in routine clinical application is visual scoring by a radiologist or an orthopedist. However, there are large uncertainties in the visual scoring since no consensus regarding the criteria for assessing cartilage damage has been reached. Thus, automatic detection and classification of the cartilage regions is required. Although some state-of-the-art methods are reported for this purpose, they rely on segmenting cartilage surfaces, which requires segmenting the cartilage tissue as well. Current approaches for cartilage segmentation are based on features like the texture, edges and sharpness of the image. However, a segmentation based on these features can easily fail, especially in the presence of inhomogeneities, due to local contrast variations and shape discontinuities caused by the complex and rough cartilage surface. In this paper, we propose a segmentation method based on both the intensity and the shape information. This is achieved by regularization of the Cartilage Embedded Gaussian Scale Mixture model (CEGM) for the representation of bone and cartilage. Our method, called CartilageSEG, is computationally more efficient than the state-of-the-art approach for cartilage segmentation, as it avoids the computation of the inversion of a scale mixture of Gaussian distributions. Furthermore, it is
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