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

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

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2) for the prior probability distribution for the underlying label image L. Now a more complex model for L is used, more specifically a MRF. Such a MRF model assumes that the probability that voxel j belongs to tissue type k depends on the tissue type of its neighbors. e. its configurations obey a Gibbs distribution f (L | ) = Z( )−1 exp[−U (L | where Z( ) = Σ L exp[−U (L | tition function and U (L | parameters )] )] is a normalization constant called the par- ) is an energy function dependent on the model .

Scans 1 and 2 are two consecutive scans from one patient, 3 and 4 from the next and so on. Note that in 9 out of 10 cases, the two ratings agree over the direction of the change of the TLL over time. 19 displays the MR data of what is called scan 19 in Fig. 18(b) and the automatically calculated classification along with the lesion delineations performed by the human expert. 3 Discussion Most of the methods for MS lesion segmentation described in the literature are semiautomated rather than fully automated methods, designed to facilitate the tedious task of manually outlining lesions by human experts, and to reduce the inter- and intrarater variability associated with such expert segmentations.

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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