Docteur de l'université Automatic Segmentation and Shape Analysis ...

48 Chapter 3 Hippocampal segmentation using multiple atlases
are not propagated to the segmentation result. The atlases to be fused into the
segmentation result are selected by their similarity to the query. Three similarity
criteria were discussed by Aljabar et al. (2007, 2009), namely
segmentation similarity between the warped label map Lk ◦Tk and the manual
segmentation of the query, which is not realistic in the application since it
prejudges the outcome of the segmentation, and assumes the availability of
the ground truth,
image similarity between the warped atlas Ik ◦Tk and the query image I, which
is usually used to evaluate the quality and accuracy of the registration,
demographic information between the atlas subject k and the query subject,
which is independent of the image, and registration results.
Among the three similarity criteria, the selection by image similarity is the most
popular in applications. The warped atlases {Ik ◦Tk} are compared with the target
image I, and ranked according to the similarity. The atlases most similar to the
target image I are selected. Due to the longer computation time of NRR than the
rigid-affine registration, sometimes the atlases are selected based on the rigid-affine
results {Ik ◦ T A k }, and only selected atlases in A are registered non-rigidly to I,
and combined to produce the segmentation result subsequently. Commonly used
image similarity metrics include sum of squared differences (SSD), correlation
coefficient, mutual information (Collignon et al., 1995; Viola and Wells, 1995),
normalized mutual information (NMI, Studholme et al., 1996), etc. The NMI and
correlation coefficient are found to provide better estimates for the accuracy of the
warped atlases (Aljabar et al., 2007, 2009).
3.1.2.1 Image mutual information
Image mutual information is a widely used similarity metric in image registration
(for review, see Pluim et al., 2003). It is able to measure the alignment between