In this paper, we propose a new region growing algorithm called 'probabilistic region growing (PRG)' which could improve the magnetic resonance image (MRI) segmentation. The proposed approach includes a threshold based on estimating the probability of pixel intensities of a given image. This threshold uses a homogeneity criterion which is obtained automatically from characteristics of the regions. The homogeneity criterion will be calculated for each pixel as well as the probability of pixel value. The proposed PRG algorithm selects the pixels sequentially in a random walk starting at the seed point, and the homogeneity criterion is updated continuously. The proposed PRG algorithm is applied to the challenging applications: grey matter/white matter segmentation in MRI data sets. The experimental results compared with other segmentation techniques show that the proposed PRG method produces more accurate and stable results. © 2013 © 2013 Taylor & Francis.