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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. 4 in order to quantify lesions or disease-related signal abnormalities in the images (see Fig. 5(d)). Adding an explicit model for the pathological tissues is difficult because of the wide variety of their appearance in MR images, and because not every individual scan contains sufficient pathology for estimating the model parameters.

8), which is simply the volume of intersection of the two segmentations divided by the mean of the two segmentation volumes. For the 20 scans, Fig. 18(a) depicts the value of this index for varying κ, both with and without the bias correction step included in the algorithm, clearly demonstrating the need for bias field correction. 45, was found for κ 3. For this value of κ, the automatic TLL was virtually equal to the expert TLL, and therefore, a similarity index of 35 Model-Based Brain Tissue Classification similarity index vs.

This avoids all manual intervention, yielding fully objective and reproducible results. Moreover, it eliminates the danger of overlooking some tissue types during a manual training phase, which is typically a problem in regions surrounding the 20 Leemput et al. brain, consisting of several different tissues, and which may cause severe errors in the residue image and the bias field estimation [6, 34]. Guillemaud and Brady [6] proposed to model nonbrain tissues by a single class with a uniform distribution, artificially assigning the nonbrain tissue voxels a zero weight for the bias estimation.

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