A method for classifying bone (202, 206, 210, 302, 306, 310) and cartilage (204, 208, 212, 314) in a magnetic resonance images of a knee, comprising: - receiving, in a data processing system, an MR image testing model, the testing model including unclassified bone (202, 206, 210, 302, 306, 310) and cartilage (204, 208, 212, 314) portions; - determining, by the data processing system, a testing volume of the testing model, the testing volume including areas of the testing model to be classified as bone (202, 206, 210, 302, 306, 310) or cartilage (204, 208,212,314); - modifying the testing model, by the data processing system, so that the testing volume corresponds to a mean shape and a shape variation space of an active shape model; - producing an initial classification of the testing volume into bone areas and cartilage areas, by the data processing system, by fitting the testing volume to the mean shape and the shape variation space of the active shape model; - producing a refined classification of the testing volume into bone areas and cartilage areas, by the data processing system, by refining the boundaries of the testing volume with respect to the active shape model; - segmenting the MR image testing model into different areas corresponding to bone areas and cartilage areas according to the refined classification; and - performing cartilage (204, 208, 212, 314) classification using: feature extraction comprising extracting - intensity features including voxel intensity and its gradient magnitude; - distance features measuring the signed Euclidean distances of each voxel to the different knee bone boundaries; and - context features comparing the intensity of the current voxel and another voxel with random offset; iterative semantic context feature boosting comprising a multi-pass iterative classification process to automatically exploit the semantic context for multiple object segmentation problems so as to classify each voxel in the image into one of the four classes, femoral cartilage, tibial cartilage, patellar cartilage and background; and post-processing by graph cut optimization using the probabilities of said four classes to construct the energy function and perform the multi-label graph cut to refine the segmentation result with smoothness constraints. - receiving, in a data processing system, an MR image testing model, the testing model including unclassified bone (202, 206, 210, 302, 306, 310) and cartilage (204, 208, 212, 314) portions; - determining, by the data processing system, a testing volume of the testing model, the testing volume including areas of the testing model to be classified as bone (202, 206, 210, 302, 306, 310) or cartilage (204, 208,212,314); - modifying the testing model, by the data processing system, so that the testing volume corresponds to a mean shape and a shape variation space of an active shape model; - producing an initial classification of the testing volume into bone areas and cartilage areas, by the data processing system, by fitting the testing volume to the mean shape and the shape variation space of the active shape model; - producing a refined classification of the testing volume into bone areas and cartilage areas, by the data processing system, by refining the boundaries of the testing volume with respect to the active shape model; - segmenting the MR image testing model into different areas corresponding to bone areas and cartilage areas according to the refined classification; and - performing cartilage (204, 208, 212, 314) classification using: feature extraction comprising extracting - intensity features including voxel intensity and its gradient magnitude; - distance features measuring the signed Euclidean distances of each voxel to the different knee bone boundaries; and - context features comparing the intensity of the current voxel and another voxel with random offset; iterative semantic context feature boosting comprising a multi-pass iterative classification process to automatically exploit the semantic context for multiple object segmentation problems so as to classify each voxel in the image into one of the four classes, femoral cartilage, tibial cartilage, patellar cartilage and background; and post-processing by graph cut optimization using the probabilities of said four classes to construct the energy function and perform the multi-label graph cut to refine the segmentation result with smoothness constraints. feature extraction comprising extracting - intensity features including voxel intensity and its gradient magnitude; - distance features measuring the signed Euclidean distances of each voxel to the different knee bone boundaries; and - context features comparing the intensity of the current voxel and another voxel with random offset; - intensity features including voxel intensity and its gradient magnitude; - distance features measuring the signed Euclidean distances of each voxel to the different knee bone boundaries; and - context features comparing the intensity of the current voxel and another voxel with random offset; iterative semantic context feature boosting comprising a multi-pass iterative classification process to automatically exploit the semantic context for multiple object segmentation problems so as to classify each voxel in the image into one of the four classes, femoral cartilage, tibial cartilage, patellar cartilage and background; and post-processing by graph cut optimization using the probabilities of said four classes to construct the energy function and perform the multi-label graph cut to refine the segmentation result with smoothness constraints.