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Chapter 3 Hippocampal segmentation using multiple atlases 45
atlases, the result segmentation ˆ L for the image I can be obtained by combining
the transformed label maps
{Lk ◦ Tk : k = 1, 2, · · · , n}. (3.5)
The mis-alignment in the transformed atlases due to the registration error is prop-
agated to the segmentation result, which can be reduced by selecting the atlases
according to the registration accuracy measured by image similarity. In the simi-
larity based atlas selection, the atlases are ranked by the image similarity between
the target image I and the registration result Ik◦Tk. The subset A ⊂ {1, 2, · · · , n}
of total n atlases best registered to the target is selected and combined in the fu-
sion step. Given the selected atlases, the labeling of the query image is determined
by the consensus of transformed segmentations. Vote rule is a simple but robust
method to produce the consensus segmentation, in which the label for each voxel
ˆL(x) is estimated as the label that accounts for the majority in {Lk(x) : k ∈ A }.
This process of segmentation propagation using multiple atlases is illustrated in
Figure 3.1.
3.1.1 Atlas registration
The transformation T between the source IS and the target IT are estimated in two
steps. Firstly, the transform T is restricted to the space of affine transformations,
and the two images are matched by rigid and/or affine registration algorithms.
Then, after solving the affine component T A of the transform T , NRR algorithms
are used to register the image IS ◦ T A to IT . The final transform T between IS
and IT is the composition
T = T A ◦ T N , (3.6)
in which T N is the non-rigid deformation found by the NNR algorithm.