Method for analysing an image of the brain of a subject, comprising: - collection of an image in at least three dimensions of the brain of a subject, - parcellation in an automated manner of said image into regions of interest (ROIs) in a brain native reference frame characteristic of said brain, the image being sampled into voxels, each voxel being assigned to one of the ROI in the brain native reference frame, - determination in an automated manner, for each ROI, of at least one discriminating value based on image data measured on the image, said image data being representative of an anatomical or functional feature of the brain, the method being characterized in that the determination of the discriminating value comprises, for each ROI, - identification of groups of voxels belonging respectively to three of the tissues of the brain, namely the grey matter, the white matter and the cerebro-spinal fluid, according to image data of said voxels, each voxel being assigned a probability to belong to one of the three tissues, wherein the identification of groups of voxels is performed using a probability model applied to the voxels, the probability model being an algorithm for separating a mixture of Gaussians, which Gaussians correspond respectively to the three tissues: α 1 * N μ 1 σ 1 2 + α 2 * N μ 2 σ 2 2 + α 3 * N μ 3 σ 3 2 where α i is the relative weight of voxels belonging to the Gaussian i, i.e. of one group of voxels with regard to the other groups of voxels, μ i is the mean value of the Gaussian i, i.e. of one group of voxels, σ i is the standard deviation of the Gaussian i, i.e. of one group of voxels, - calculation of at least one relative parameter concerning the image data based on said image data measured for each voxel, wherein the relative parameter comprises the relative weight α i, the mean value μ i the mean value μ i and the standard deviation σ i, and - establishment of the discriminating value from one of the relative parameters or a combination thereof - collection of an image in at least three dimensions of the brain of a subject, - parcellation in an automated manner of said image into regions of interest (ROIs) in a brain native reference frame characteristic of said brain, the image being sampled into voxels, each voxel being assigned to one of the ROI in the brain native reference frame, - determination in an automated manner, for each ROI, of at least one discriminating value based on image data measured on the image, said image data being representative of an anatomical or functional feature of the brain, the method being characterized in that the determination of the discriminating value comprises, for each ROI, - identification of groups of voxels belonging respectively to three of the tissues of the brain, namely the grey matter, the white matter and the cerebro-spinal fluid, according to image data of said voxels, each voxel being assigned a probability to belong to one of the three tissues, wherein the identification of groups of voxels is performed using a probability model applied to the voxels, the probability model being an algorithm for separating a mixture of Gaussians, which Gaussians correspond respectively to the three tissues: α 1 * N μ 1 σ 1 2 + α 2 * N μ 2 σ 2 2 + α 3 * N μ 3 σ 3 2 where α i is the relative weight of voxels belonging to the Gaussian i, i.e. of one group of voxels with regard to the other groups of voxels, μ i is the mean value of the Gaussian i, i.e. of one group of voxels, σ i is the standard deviation of the Gaussian i, i.e. of one group of voxels, - calculation of at least one relative parameter concerning the image data based on said image data measured for each voxel, wherein the relative parameter comprises the relative weight α i, the mean value μ i the mean value μ i and the standard deviation σ i, and - establishment of the discriminating value from one of the relative parameters or a combination thereof