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By Whyburn G. T.

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A unique feature of our algorithm is that it automatically adapts its intensity models and contextual constraints when analyzing images that were acquired with a different MR pulse sequence or voxel size. Zijdenbos et al. described [64] and validated [65] a fully automated pipeline for MS lesion segmentation based on an artificial neural network classifier. Similarly, Kikinis, Guttmann et al. [62, 66] have developed a method with minimal user intervention that is built on the EM classifier of Wells et al.

Indeed, in this particular case, the affinely matched atlas labels large portions of the enlarged ventricles as white matter. e. mean and variance) is thus not reliable and it is therefore unlikely that the iterative 10 Leemput et al. 4: Top row (from left to right): Original T1 MPRAGE patient image; CSF segmented by atlas-guided intensity-based tissue classification using affine registration to atlas template; CSF segmented after nonrigid matching of the atlas. Middle row: Atlas priors for gray matter, white matter, and CSF affinely matched to patient image.

This was clearly improved when the bias field correction step was added (Fig. 12(c)). However, some tissues surrounding the brain have intensities that are similar to brain tissue and are wrongly classified as gray matter. 1, a better distinction is obtained between brain tissues and tissues surrounding the brain (Fig. 12(d)). This is most beneficial in case of single-channel MR data, where it is often difficult to differentiate such tissues based only on their intensity. The MRF cleans up the segmentations of brain tissues, while preserving the detailed interface between gray and white matter, and between gray matter and CSF.

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