Abstract
In this paper, we propose a model for image segmentation based on a finite mixture of Gaussian distributions. For each pixel of the image, prior probabilities of class memberships are specified through a Gibbs distribution, where association between labels of adjacent pixels is modeled by a class-specific term allowing for different interaction strengths across classes. We show how model parameters can be estimated in a maximum likelihood framework using Mean Field theory. Experimental performance on perturbed phantom and on real benchmark images shows that the proposed method performs well in a wide variety of empirical situations.
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Alfò, M., Nieddu, L. & Vicari, D. A finite mixture model for image segmentation. Stat Comput 18, 137–150 (2008). https://doi.org/10.1007/s11222-007-9044-9
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DOI: https://doi.org/10.1007/s11222-007-9044-9