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Degeneration of knee joint cartilage is an important and early indicator of osteoarthritis (OA) which is one of the major socio-economic burdens nowadays. Accurate quantification of the articular cartilage degeneration in an early stage using MR images is a promising approach in diagnosis and therapy for this disease. Particularly, volume and thickness measurement of cartilage tissue has been shown to deliver significant parameters in assessment of pathologies. Here, accurate computer-aided diagnosis tools could improve the clinical routine where image segmentation plays a crucial role. In order to overcome the time-consuming and tedious work of manual segmentation, one tries to automate the segmentation as much as possible.
We focusing on novel atlas-based segmentation methods for knee cartilage as well as improving the today's clinical routine of manual segmentation methods. In addition, we try evaluate different methods for the assessment of parameters such as volume and thickness which could allow computer-aided diagnosis of knee cartilage pathologies in an early stage.
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