We propose a method for the automated computation of the mid-sagittal plane of the brain in diffusion tensor
MR images. We estimate this plane as the one that best superposes the two hemispheres of the brain by reflection
symmetry. This is done via the automated minimisation of a correlation-type global criterion over the tensor
image. The minimisation is performed using the NEWUOA algorithm in a multiresolution framework. We
validate our algorithm on synthetic diffusion tensor MR images. We quantitatively compare this computed plane
with similar planes obtained from scalar diffusion images (such as FA and ADC maps) and from the B0 image
(that is, without diffusion sensitisation). Finally, we show some results on real diffusion tensor MR images.
We propose to use a recently introduced optimisation method in the context of rigid registration of medical
images. This optimisation method, introduced by Powell and called NEWUOA, is compared with two other
widely used algorithms: Powell's direction set and Nelder-Mead's downhill simplex method. This paper performs
a comparative evaluation of the performances of these algorithms to optimise different image similarity measures
for different mono- and multi-modal registrations. Images from the BrainWeb project are used as a gold standard
for validation purposes. This paper exhibits that the proposed optimisation algorithm is more robust, more
accurate and faster than the two other methods.