[BIC-announce] Rachid Deriche's Group - talk on Monday at 4pm

[Kaleem Siddiqi]

Dear Colleagues,
Rachid Deriche and his group will be visiting us on Monday and  
Tuesday next week and
we have scheduled a talk for Monday afternoon, which should be of  
broad interest
to those working in the area of medical imaging.

Date: Monday, April 3rd.
Time and Place: 4pm, Grandpre Communication Centre, Montreal  
Neurological Institute
	(http://www.mni.mcgill.ca/dgpcentre/)

Title: A Statistical Framework for DTI Segmentation
Authors: Christophe Lenglet; INRIA
                 Mikaël Rousson; Siemens Corporate Research, Inc.
                 Rachid Deriche; INRIA


Abstract:
We address the problem of the segmentation of cerebral white matter  
structures from diffusion tensor images (DTI). DTI can be estimated  
from a set of diffusion weighted images and provides tensor-valued  
images where each voxel is assigned with a 3x3 symmetric, positive- 
definite matrix. As we will show in this paper, the definition of a  
dissimilarity measure and statistics between tensors is a non trivial  
task which must be carefully tackled. We claim that, by using the  
differential geometrical properties of the manifold of multivariate  
normal distributions, it is possible to improve the quality of the  
segmentation obtained with other dissimilarity measures such as the  
Euclidean distance or the Kullback-Leibler divergence. Our goal is to  
prove that the choice of this probability metric has a deep impact on  
the tensor statistics and, hence, on the achieved results. We  
introduce a variational formulation to estimate the optimal  
segmentation of a diffusion tensor image. We show how to estimate  
diffusion tensors statistics for three different probability metrics  
and evaluate their respective performances. We validate and compare  
the results obtained on synthetic and real datasets.

Best Regards,
-Kaleem

Kaleem Siddiqi
School of Computer Science & Centre For Intelligent Machines
McGill University
http://www.cim.mcgill.ca/~siddiqi

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