Demonology: contributions to the Demons' image registration algorithm
Image registration consists in finding the geometric transformation that best superimposes the homologous points (voxels for 3D images) of two images. Originally proposed by Jean-Philippe Thirion in 1998 as an
efficient procedure for non-linear registration, the demons' algorithm was revisited during 20 years.
Explanation as an alternated direction minimization method
With Pascal Cathier, we recast the demons as an alternated direction minimization method. This gave rise to Pascal's pair and smooth (PASHA) method.
- X. Pennec, P. Cachier, and N. Ayache. Understanding the ``Demon's Algorithm'':
3D Non-Rigid registration by Gradient Descent. In
Proc. of 2nd Int. Conf. on Medical Image Computing and Computer-Assisted Intervention (MICCAI'99),
volume 1679 of LNCS, Cambridge, UK, pages 597-605, September 1999. Springer. [PDF].
Keywords:Explanation of the correspondence phase of the demons as a second order gradient descent of the SSD criterion.
- P. Cachier and X. Pennec. 3D Non-Rigid Registration by Gradient
Descent on a Gaussian-Windowed Similarity Measure using Convolutions.
In Proc. of IEEE Workshop on Mathematical Methods in Biomedical Image Analysis (MMBIA'00),
Hilton Head Island, South Carolina, USA, pages 182-189, June 2000. IEEE Computer society.
Keywords:Introduction of the local correlation criterion (LCC).
- P. Cachier, J.-F. Mangin, X. Pennec, D. Rivière,
D. Papadopoulos-Orfanos, J. Régis, and N. Ayache.
Multisubject Non-Rigid Registration of Brain MRI using Intensity and Geometric Features.
In Proc of Medical Image
Computing and Computer-Assisted Intervention (MICCAI'01), volume 2208 of LNCS, Utrecht,
The Netherlands, pages 734-742, October 2001.
Keywords:Combining feature-based and intensity-based registration.
- P. Cachier, E. Bardinet, D. Dormont, X. Pennec, and N. Ayache.
Iconic Feature Based Nonrigid Registration: The PASHA Algorithm.
Comp. Vision and Image Understanding, 89(2-3):272-298, Feb.-march 2003. [PDF]
Keywords:The general matching / transformation estimation alternated framework.
With Tom Vercauteren, we adapted the efficient optimization procedure to work on a space of
diffeomorphic transformations. Experiments show that the diffeomorphic demons results are
similar in terms of image similarity metric, but more regular and closer to the true
transformation in controlled experiments, particularly in terms of Jacobian.
- T Vercauteren, X Pennec, A Perchant, N Ayache. Diffeomorphic demons: Efficient non-parametric image registration.
NeuroImage 45 (1), S61-S72. [PDF]
An early version of this methods was presented at MICCAI 2007.
- BTT Yeo, T Vercauteren, P Fillard, JM Peyrat, X Pennec, P Golland, Nicholas Ayache, Olivier Clatz.
DT-REFinD: Diffusion tensor registration with exact finite-strain differential.
IEEE transactions on medical imaging 28 (12), 1914-1928, 2009.
Keyword: Diffeomorphic demons extended to match diffusion tensor images.
Log demons in the SVF framework
The parametrization of diffeomorphisms by the flow of stationnary velocity fields (the SVF framework for diffeomorphism) was introduced at MICCAI 2006 by Arsigny. Combined with the BCF formula proposed by Bossa at MICCAI 2007, this led to a new formulation of the demons which optimizes directly the SVF parameterizing the deformation.
This new parameterization allows to obtain inverse consistency very easily, and opens the way to a sound statistical setting for deformation-based morphometry.
- V. Arsigny, O. Commowick, X.Pennec, N. Ayache. A log-euclidean framework for statistics on diffeomorphisms Proc. of MICCAI 2006, LNCS 4190 pp 924--931, Springer, 2006. [PDF]
- T Vercauteren, X Pennec, A Perchant, N Ayache. Symmetric log-domain diffeomorphic registration: A demons-based approach. Proc of Medical Image Computing and Computer Assisted Intervention (MICCAI) 2008, Part I, Sep 2008, New York, United States. Springer, LNCS 5241, pp.754--761, 2008.
- M Lorenzi, N Ayache, GB Frisoni, X Pennec.
LCC-Demons: a robust and accurate symmetric diffeomorphic registration algorithm.
NeuroImage 81, 470-483, Nov 2013.