Last update: August 3, 2005
Publications by Jean-Francois Mangin*
et al. on shape symmetry elicitation in Medical Image Analysis:
- Object-Based Morphometry of the
Cerebral Cortex, 2004.
- Shape
bottlenecks and conservative flow systems, 1996.
- From 3D magnetic
resonance images to structural representations of the cortex topography
using topology preserving deformations, 1995.
*Affiliations: Service Hospitalier
Frédéric Joliot, Commissariat à l'Énergie
Atomique (CEA), 91401 Orsay Cedex, France
Département Images, École Nationale Supérieure des
Télécommunications, 75634 Paris Cedex 13, France
BibTeX references.
Object-Based Morphometry of the
Cerebral Cortex
J.-F. Mangin, D.
Rivière, A. Cachia, D. Papadopoulos-Orfanos (CEA), D.
L. Collins, A. C. Evans (Montreal Neurological Institute) and J.
Régis
(Hôpital d'adulte de la Timone).
Lecture Notes in Computer Science.
Berlin, Germany: Springer Verlag, 2003, vol.
2732, Proc. IPMI, pp. 160–171, 2003.
Also in
IEEE Transactions on Medical Imaging, Vol. 23, No.8, August 2004.
Abstract
Most of the approaches dedicated to
automatic morphometry rely on a point-by-point strategy
based on warping each brain towards a reference coordinate system. In
this paper, we describe
an alternative object-based strategy dedicated to the cortex. This
strategy relies on
an artificial neuroanatomist performing automatic recognition of the
main cortical
sulci and parcellation of the cortical surface into gyral patches.
A set of shape descriptors, which can be compared across subjects, is
then attached to the
sulcus and gyrus related objects segmented by this process.
The framework is used to perform a study of 142 brains
of the ICBM database. This study reveals some correlates of handedness
on the size of the sulci located in
motor areas, which seem to be beyond the scope of the standard voxel
based morphometry.
Shape
bottlenecks and conservative flow systems
J.-F.
Mangin, J. Regis and V. Frouin
Proceedings of the 1996 Workshop on Mathematical Methods in Biomedical
Image Analysis
Abstract
This paper proposes an alternative to mathematical
morphology to analyze complex shapes. This approach aims mainly at
the detection of shape bottlenecks which are often of interest in
medical imaging because of their anatomical meaning. The detection idea
consists in simulating the steady state of an information transmission
process between two parts of a complex object in order to highlight
bottlenecks as areas of high information flow. This information
transmission process is supposed to have a conservative flow which
leads to the well-known Dirichlet-Neumann problem. This problem is
solved using finite differences, over-relaxation and a raw to fine
implementation. The method is applied to the detection of main
bottlenecks of brain white matter network, namely corpus callosum,
anterior commissure and brain stem.
From 3D magnetic resonance
images to structural representations of the cortex topography using
topology preserving deformations
Keywords: medical imaging, mathematical morphology,
deformable contour, Markovian random fields, topology preserving
deformation, structural pattern recognition, functional brain mapping
Summary
They extract a 3D skeleton of deep sulci, based on Markov Random
Fields, parse it into an Attributed Relational Graph (ARG) of connected
surface elements. Then they define a syntactic energy on the space of
associations between the surface elements and anatomic labels, from
which estimates of correct labelings - and therefore correct matches
across subjects - can be derived.
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