Statistical Methods for Brain Segmentation

The performance and limitations of many supervised and unsupervised statistical methods for MR segmentation are discussed in a review by Bezdek et al. [6].

Kapur et al. [19] segment the brain in 3D gradient-echo MR images by combining the statistical classification of Wells et al. [34] with image processing methods. A single channel, non-parametric, multiclass implementation of Well's classifier based on tissue type training points is used to classify brain tissues. Further morphological processing is needed to remove connected nonbrain components. The established brain contours are refined using an algorithm based on snakes. The combination of statistical classification of brain tissue, followed by morphological operators, is effective in segmenting the brain from other structures such as orbits in a semiautomated fashion. Furthermore, Wells's statistical classification method also reduces the effect of RF inhomogeneity. However, Kapur's method requires some interaction to provide tissue training pixels, and in 10% of volumes studied interaction is needed to remove nonconnected brain tissue. The method is computationally intensive and has onlybeen used on 3D T1 gradient-echo data with slice thickness of 1.5 mm. Its performance on PD-T2 images with slice thickness of 5 mm remains to be determined.

Stringham et al. use the gradient magnitude as well as voxel intensity with a statistical relaxation method for brain segmentation [29]. This segmentation algorithm is robust in the presence of RF inhomogeneity, but may not be able to distinguish the brain form other tissues such as the eyes, as do most Bayesian relaxation-based techniques [18]. User interaction is required to seed the relaxation process.

Fuzzy-connectedness methods developed by Udupa and Samarasekera [31] are based on knowledge of tissue intensities, followed by statistical region-growing methods. This method has been successfully used to segment the brain in 1000 multiple sclerosis patients [32], but is rather computationally intensive. Again, user interaction is required to seed the regions.

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