Genetic Algorithms for Finite Mixture Model Based Tissue Classification in Brain Mri


Finite mixture models (FMMs) are an indispensable tool for unsupervised classification in brain imaging. Fitting a FMM to the data leads to a complex optimization problem. This optimization problem is difficult to solve with standard local optimization methods (e.g. by the expectation maximization (EM) algorithm) if a good initialization is not available… (More)


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