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Tissue deterioration as induced by disease can be viewed as a continuous change of tissue from healthy to diseased and hence can be modeled as a non-linear manifold with completely healthy tissue at one end of the spectrum and fully abnormal tissue such as lesions, being on the other end. The ability to quantify this tissue deterioration as a continuous score of tissue abnormality will help determine the degree of disease progression and treatment effects. We propose a semi-supervised method for determining such an abnormality manifold, using multi-parametric magnetic resonance features incorporated into a support vector machine framework in combination with manifold regularization. The position of a tissue voxel on this spatially and temporally smooth manifold, determines its degree of abnormality. We apply the framework towards the characterization of tissue abnormality in brains of multiple sclerosis patients followed longitudinally, to obtain a voxel-wise score of abnormality called the tissue abnormality map, thereby obtaining a voxel-wise measure of disease progression.