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Hello, I was going through the 2005 paper on unified segmentation by Ashburner and Friston. I was hoping to clarify a couple of points from the paper. So the paper models intensity distribution using a mixture of gaussians. So my questions are as follows 1. Do the different clusters(Gaussians) correspond to different tissue types in the brain viz., Grey Matter, White Matter and CSF? i.e. one is interested in measuring the probability of a voxel of a given intensity belonging to a certain tissue class? 2. In the same paper it is mentioned that 'In a simple MOG, the probability of obtaining a datum with intensity yi given that it belongs to the kth Gaussian (ci = k) and that the kth Gaussian is parameterised by nu_k and sigma_k^2' is given by the probability density function of a gaussian distribution. But since we are dealing with a continuous data set of intensity, the probability that the voxel intensity is equal to a certain value is zero, we can only ask what is the probability that the intensity is in a certain range and that too will be given by the integral of the gaussian function in that range. Could someone please clarify these points for me, especially the second question. Thanks, Sasha