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

44 Chapter 3 Hippocampal segmentation using multiple atlases
Processing, and “Atlas selection strategy using least angle regression in multi-atlas
segmentation propagation,” in 2011 IEEE International Symposium on Biomedical
Imaging.
3.1 Basic version of multi-atlas based segmentation propa-
gation
In image analysis, the 3D image I acquired by structural MR scans is modeled by
a real function defined on a rectangular region Ω ⊂ R 3
I : Ω ↦→ R, (3.1)
such that the intensity of each voxel x ∈ Ω is given by I(x). The aim of image
segmentation is to create a label map
L : Ω ↦→ L , (3.2)
for image I such that each voxel x is identified with a label L(x) in the label set
L .
In multi-atlas based segmentation, we use an atlas set
{(Ik, Lk) : k = 1, 2, · · · , n} (3.3)
in which each atlas image Ik : Ωk ⊂ R 3 ↦→ R is labeled with known segmentation
Lk : Ωk ↦→ L , to segment the query I. Each atlas image is first registered to I,
producing the transformation
Tk : Ω ↦→ Ωk
(3.4)
mapping the atlas Ik to the transformed image Ik ◦ Tk : Ω ↦→ R in the same space
of the target I. The same transformation can be applied to Lk such that the
transformed label map Lk ◦ Tk gives one labeling for the image I. Using multiple