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Recent advances in magnetic resonance (MR) imaging have made it the modality of choice for assessment of cartilage status for osteoarthritis (OA) (Roemer et al., 2009; Eckstein, 2007). MRI allows assessment of morphological changes using high resolution 3D imaging as well as evaluation of changes at the biochemical/molecular level using MR relaxometry techniques (T2: spin-spin relaxation and T1: spin-lattice relaxation with spin locking) and delayed Gadolinium enhanced MRI of cartilage (dGEMRIC) (Blumenkrantz & Majumdar, 2007). Research on the potential application of MRI to detect and classify osteoarthritis and monitor progression is an area of intense research (Hunter et al., 2009). A number of review articles detail the technical limits, accuracy and precision of MRI including the choice of pulse sequences and image analysis methods for morphological assessment in osteoarthritis (Raynauld, 2003; Eckstein et al., 2009; Xing, 2011). Cartilage morphology metrics from MRI are now being actively explored as imaging biomarkers of disease status and progression. The sequences that provide the best contrast of cartilage to adjacent tissue as well as the best resolution are the fat-suppressed spoiled gradient echo and fast double echo and steady state (DESS) with water excitation (Blumenkrantz & Majumdar, 2007; Roemer et al., 2010). Segmentation of the cartilage is a challenging task and few completely automated segmentation algorithms have been proposed. A fully automated segmentation algorithm based on Active Shape Modeling (Fripp et al., 2007) has been implemented and evaluated for normal cartilage. A more recent automated algorithm that is extensible to osteoarthritic cartilage first segments the bone-cartilage interface and then classifies cartilage voxels based on texture analysis. However, the large-scale applicability of these algorithms to normal and diseased cartilage is not yet demonstrated. Semiautomated algorithms using region growing, edge detection, and spline fitting have been used successfully in large-scale projects (Raynauld, 2003; Duryea et al., 2007). Morphological indices used to classify disease status and progression, include global and regional measures of total cartilage volume (raw and normalized), cartilage surface area, cartilage thickness (global and regional), and denuded area (Pelletier et al., 2007; Eckstein et

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An Atlas-Based Approach to Study Morphological Differences in Human Femoral Cartilage Between Subjects from Incidence and Progression Cohorts: MRI Data from Osteoarthritis Initiative