@conference {cHaro08,
	title = {On the non-uniform complexity of brain connectivity},
	booktitle = {5th IEEE International Symposium on Biomedical Imaging (ISBI 2008)},
	year = {2008},
	month = {05/2008},
	publisher = {IEEE},
	organization = {IEEE},
	address = {Paris},
	abstract = {<p>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.</p>},
	keywords = {biodiffusion, biological tissues, biology computing, brain, brain connectivity, cellular biophysics, cerebral white matter, Clustering methods, complex microstructure, density, Density measurement, Diffusion tensor imaging, Geometry, high-angular resolution diffusion imaging, high-dimensional spaces, High-resolution imaging, Image analysis, Image resolution, Magnetic resonance imaging, manifold learning approach, Microstructure, molecular biophysics, nonuniform complexity, Point processes, Poisson processes, Signal resolution, stratification learning, Switches, Tensile stress, Unsupervised learning},
	doi = {10.1109/ISBI.2008.4541139},
	author = {Haro, G. and Lenglet, C. and Sapiro, Guillermo and Thompson, P.}
}
