Docteur de l'université Automatic Segmentation and Shape Analysis ...
Docteur de l'université Automatic Segmentation and Shape Analysis ...
Docteur de l'université Automatic Segmentation and Shape Analysis ...
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Chapter 4 Statistical shape mo<strong>de</strong>l of Hippocampus 73<br />
U × V<br />
γi ✲<br />
γj<br />
U × V<br />
✲<br />
.<br />
.<br />
.<br />
U × V<br />
fi ✲ Xi<br />
✻<br />
fj<br />
❄<br />
✲ Xj<br />
correspon<strong>de</strong>nce<br />
Figure 4.1: Parameterizations {fi} <strong>and</strong> reparameterizations {γi} of the shape<br />
surfaces {Xi} <strong>and</strong> correspon<strong>de</strong>nces.<br />
achieved by reparameterization of {fi}<br />
on the parameter space, such that<br />
γi : U × V ↦→ U × V, i = 1, · · · , n (4.2)<br />
γi(uhi , vhi ) = γj(uhj , vhj ) = (uh, vh), (4.3)<br />
<strong>and</strong> the result collection of parameterizations {fi◦γi} establish the correspon<strong>de</strong>nce<br />
across the training data {Xi}.<br />
Since the hippocampal segmentation are simply connected regions on MR images,<br />
the closed surfaces extracted from the segmentations are homeomorphic to the S 2<br />
sphere. The reparameterizations are thus homeomorphisms on S 2<br />
in which a rotation component Γ can be separated<br />
γi : S 2 ↦→ S 2 , i = 1, · · · , n, (4.4)<br />
γi = Γi ◦ ˜γi<br />
(4.5)<br />
We introduce the calculation of the parameterization f in §4.1.1. To establish