The majority of methods that have been proposed for automated segmentation of brain tissues are based on statistical parametric models. The MPM-MAP (Maximizer of the posterior marginals- Maximum a posteriori) algorithm  exemplifies this approach. It implements Bayesian segmentation based on non-rigid registration of the atlas[AM1] . The algorithm uses Expectation-Maximization (EM) for estimation of model parameters and Hidden Markov Random Fields (HMRF) for spatial coherences. Two other examples are KVL (K. Van Leemput)  and CGMM (Constrained Gaussian mixture model)  which use maximum a posteriori probability (MAP) or maximum likelihood (ML) method for the estimation of model parameters. A drawback with these approaches is that it is difficult to integrate pixel spatial information with multi-valued pixel information (e.g. when several different MR scans have been acquired). This is because the HMRF is itself hard to implement[AM2] in high dimensional feature space.
Mean-shift (MS) segmentation overcomes this drawback. Mean-shift [4, 5] is a non-parametric technique used to estimate the modes of the multivariate distribution underlying a feature space. It does not require any prior information to initialize the position of the clusters and also does not constraint the shape of the clusters. Mean-shift segmentation involves concatenating both the spatial and range domains of an image and identifying modes in this multidimensional joint spatial-range feature space. The only free parameter is the kernel size (called the bandwidth). If the chosen value is too small then insignificant modes are detected (over-clustering). If it is too large then significant modes can be merged (under-clustering). Several methods  are available for the estimation of a single fixed bandwidth. However, the use of a single fixed bandwidth has the drawback that it can yield under- or over-clustering when the feature space has significantly different local characteristics across the space. Variable or adaptive bandwidth methods have been proposed  to overcome this drawback. Such methods involve determining the bandwidth value for each feature point by using the pilot density estimate[AM3] . In  it was shown that adaptive mean-shift clustering, in which the pilot density estimate is based on the nearest neighborhood, performs better than a single fixed bandwidth in high dimensional space. Nevertheless the bandwidth value defined in terms of the Euclidean distance to the farthest feature point from the kernel center can be biased by outliers .
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