By Quas A. N.

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17: A 3-D rendering of (a) gray matter, (b) white matter, and (c) MS lesion segmentation maps. (Color slide) a T1-weighted image with 5 mm slice thickness. From 10 of the patients, two consecutive time points were manually analyzed by a human expert who traced MS lesions based only on the T2-weighted images. 05. The automatic delineations were compared with the expert segmentations by comparing the so-called total lesion load (TLL), measured as the number of voxels that were classified as MS lesion, on these 20 scans.

The properly registered and reformatted a priori tissue probability maps of the atlas provide an initial estimate of the classification from which initial values for the class-specific distribution parameters µk and Σk can be computed. This approach frees us from having to interactively indicate representative voxels of each class, which makes our method more objective and reproducible and allows the method to be fully automated. The classification and intensity distribution parameters are then updated using the iterative scheme based on the EM procedure as outlined above.

From left to right: (a) axial and coronal slice, (b) corresponding manual segmentation of gray matter, (c) automatic segmentation of gray matter without MRF prior, (d) automatic segmentation of gray matter with MRF, and (e) difference between manual and automatic segmentation with MRF shown in white. (Source: Ref. 4 The segmented image for each case was compared with the corresponding expert manual segmentation by means of an overlap metric. The overlap measure is simply the intersection of two coregistered images divided by their union.

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