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Handbook of Biomedical Image Analysis, Vol.2: Segmentation by Jasjit S. Suri (Editor), David Wilson (Editor), Swamy

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By Jasjit S. Suri (Editor), David Wilson (Editor), Swamy Laxminarayan (Editor)

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Then the presence of pathological tissues, which are not included in the statistical model, is considered (d). Finally, a downsampling step introduces partial voluming in the model (e). Model-Based Brain Tissue Classification 13 is illustrated in Fig. 5(c), which shows a sample of the total resulting image model. The MRF brings general spatial and anatomical constraints into account during the classification, facilitating discrimination between tissue types with similar intensities such as brain and nonbrain tissues.

Although not always visible for a human observer, it can cause serious misclassifications when intensity-based segmentation techniques are used. 2 the mixture model is therefore extended by explicitly including a parametric model for the bias field. 5(b) shows a typical sample of the resulting model. The model parameters are then iteratively estimated by interleaving three steps: classification of the voxels; estimation of the normal distributions; and estimation of the bias field. The algorithm is initialized with information from a digital brain atlas about the a priori expected location of tissue classes.

As a result, the total iterative scheme now consists in four steps, shown in Fig. 11. 11: The extension of the model with a MRF prior results in a four-step algorithm that interleaves classification, estimation of the normal distributions, bias field correction, and estimation of the MRF parameters. 24 Leemput et al. The calculation of the MRF parameters poses a difficult problem for which a heuristic, noniterative approach is used. For each neighborhood configuration (N p , N o ), the number of times that the central voxel belongs to class k in the current classification is compared to the number of times it belongs to class k , for every couple of classes (k, k ).

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