L. Younes

Publications224
h-index51
Citations10,375
Highly Influential Citations660
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Computing Large Deformation Metric Mappings via Geodesic Flows of Diffeomorphisms
TLDR
The Euler-Lagrange equations characterizing the minimizing vector fields vt, t∈[0, 1] assuming sufficient smoothness of the norm to guarantee existence of solutions in the space of diffeomorphisms are derived.
Shapes and Diffeomorphisms
Parametrized Plane Curves.- Medial Axis.- Moment-Based Representation.- Local Properties of Surfaces.- Isocontours and Isosurfaces.- Evolving Curves and Surfaces.- Deformable templates.- Ordinary…
On the metrics and euler-lagrange equations of computational anatomy.
TLDR
Current experimental results from the Toga & Thompson group in growth, the Van Essen group in macaque and human cortex mapping, and the Csernansky group in hippocampus mapping for neuropsychiatric studies in aging and schizophrenia are shown.
Computable Elastic Distances Between Shapes
  • L. Younes
  • Computer Science
    SIAM J. Appl. Math.
  • 1 April 1998
TLDR
An elastic matching algorithm which is based on a true distance between intrinsic properties of the shapes, taking into account possible invariance to scaling or Euclidean transformations in the case they are required.
Geodesic Shooting for Computational Anatomy
TLDR
It is shown that this momentum can be also used for describing a deformation of given visual structures, like points, contours or images, and that, it has the same dimension as the described object, as a consequence of the normal momentum constraint the authors introduce.
On the convergence of markovian stochastic algorithms with rapidly decreasing ergodicity rates
  • L. Younes
  • Mathematics, Computer Science
  • 1 February 1999
TLDR
This work analyses the convergence of stochastic algorithms with Markovian noise when the ergodicity of the Markov chain governing the noise rapidly decreases as the control parameter tends to infinity and provides sufficient condition which ensure convergence.
Group Actions, Homeomorphisms, and Matching: A General Framework
TLDR
Left-invariant metrics are defined on the product G × I thus allowing the generation of transformations of the background geometry as well as the image values, and structural generation in which image values are changed supporting notions such as tissue creation in carrying one image to another.
Multi-contrast large deformation diffeomorphic metric mapping for diffusion tensor imaging
Geodesic Interpolating Splines
TLDR
This method is based on spline interpolation, and on recent techniques developed for the estimation of flows of diffeomorphisms, and provides a Riemannian distance on sets of landmarks (with fixed cardinality), which can be defined intrinsically, without refering to diffEomorphisms.
Ex vivo 3D diffusion tensor imaging and quantification of cardiac laminar structure
TLDR
Within anisotropic regions, two consistent and dominant orientations were identified, supporting published results from histological studies and providing strong evidence that the tertiary eigenvector of the diffusion tensor (DT) defines the sheet normal.
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