DocumentCode :
1823024
Title :
On the non-uniform complexity of brain connectivity
Author :
Haro, Gloria ; Lenglet, Christophe ; Sapiro, Guillermo ; Thompson, Paul
Author_Institution :
Univ. Politec. de Catalunya, Barcelona
fYear :
2008
fDate :
14-17 May 2008
Firstpage :
887
Lastpage :
890
Abstract :
A stratification and manifold learning approach for analyzing High Angular Resolution Diffusion Imaging (HARDI) data is introduced in this paper. HARDI data provides high- dimensional signals measuring the complex microstructure of biological tissues, such as the cerebral white matter. We show that these high-dimensional spaces may be understood as unions of manifolds of varying dimensions/complexity and densities. With such analysis, we use clustering to characterize the structural complexity of the white matter. We briefly present the underlying framework and numerical experiments illustrating this original and promising approach.
Keywords :
biodiffusion; biological tissues; biology computing; brain; cellular biophysics; molecular biophysics; biological tissues; brain connectivity; cerebral white matter; complex microstructure; density; high-angular resolution diffusion imaging; high-dimensional spaces; manifold learning approach; nonuniform complexity; stratification learning; Diffusion tensor imaging; Geometry; High-resolution imaging; Image analysis; Image resolution; Magnetic resonance imaging; Microstructure; Signal resolution; Switches; Tensile stress; Clustering methods; Density measurement; Point processes; Poisson processes; Unsupervised learning;
fLanguage :
English
Publisher :
ieee
Conference_Titel :
Biomedical Imaging: From Nano to Macro, 2008. ISBI 2008. 5th IEEE International Symposium on
Conference_Location :
Paris
Print_ISBN :
978-1-4244-2002-5
Electronic_ISBN :
978-1-4244-2003-2
Type :
conf
DOI :
10.1109/ISBI.2008.4541139
Filename :
4541139
Link To Document :
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