1. A method of analysis of multi-sequence MRI data for neurological disorders comprising: obtaining intensity information using an MRI modality; assigning a probability of a presence of a neurological abnormality using a logistic regression model; producing results in the form of regression coefficients that can be applied to the multi-sequence MRI data; adjusting for intensity inhomogeneities; sequentially blurring images comprising the multi-sequence MRI data using Gaussian blurs with varying kernel sizes resulting in blurred images; oversmoothing the images; maintaining regional features of the multi-sequence MRI data, as well as spatial inhomogeneities; removing local voxel-specific features; providing a multi-scale procedure, thereby allowing for different resolutions in terms of the regional features of interest wherein the neurological abnormalities are included in the blurred images; isolating probable abnormalities from each modality, each of the blurred images; including in a logistic regression model having estimates of voxel-specific probability of the voxel being part of the abnormality in an interaction between the images and the blurred images; removing said abnormality from the image; reblurring said image resulting in a reblurred image; providing a refined description of regional patterns including spatial inhomogeneity and inhomogenieity and underlying anatomy of said abnormalities; providing a segmentation for identifying the neurological abnormality in a subject; and locating a position of the neurological abnormality in the subject.