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

140 Chapter 6 Conclusions
6.2 Future works
In the future work, new methodologies and algorithms for brain image segmenta-
tion, shape modeling and analysis of neuroanatomical structures may be developed
to extend the current work presented in this thesis.
6.2.1 Non-local approach to the atlas based image segmentation
Non-local approach has been developed to select patches of atlases similar to the
target image. One advantage of this approach is its computational efficiency,
since non-rigid registrations of the atlases images are no longer necessary in this
approach. The label for each voxel is searched over the atlases, which provides a
larger selection pool of candidate labeling. The current strategies for atlas selection
and fusion may be extended to the patch selection and fusion in the non-local
approach. In addition to the propagation of the anatomical labeling, demographic
and diagnostic information of each atlas may also be propagated to the target
image, thus localizing the effects of the pathology as well as normal physiology on
a voxelwise basis.
6.2.2 Groupwise shape correspondence by optimization
The method for shape correspondence by optimization adopted and developed
in this thesis applies a fluid regularization to the deformation, which is similar
to the techniques widely used image registration. The current state-of-the-art in
the field of image registration provides some important insight to the solution
of the correspondence problem by optimization. A general algorithmic framework
accommodating a wide range of registration techniques including fluid registration,
elastic registration and Demons method may be implemented.