Sparse Adaptive Parameterization of Variability in Image Ensembles

Stanley Durrleman 1, 2 Stéphanie Allassonnière 3 S. Joshi 1
2 ARAMIS - Algorithms, models and methods for images and signals of the human brain
Inria Paris-Rocquencourt, UPMC - Université Pierre et Marie Curie - Paris 6, ICM - Institut du Cerveau et de la Moëlle Epinière = Brain and Spine Institute
Abstract : This paper introduces a new parameterization of diffeomorphic deformations for the characterization of the variability in image ensembles. Dense diffeomorphic deformations are built by interpolating the motion of a finite set of control points that forms a Hamiltonian flow of self-interacting particles. The proposed approach estimates a template image representative of a given image set, an optimal set of control points that focuses on the most variable parts of the image, and template-to-image registrations that quantify the variability within the image set. The method automatically selects the most relevant control points for the characterization of the image variability and estimates their optimal positions in the template domain. The optimization in position is done during the estimation of the deformations without adding any computational cost at each step of the gradient descent. The selection of the control points is done by adding a L 1 prior to the objective function, which is optimized using the FISTA algorithm.
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Stanley Durrleman, Stéphanie Allassonnière, S. Joshi. Sparse Adaptive Parameterization of Variability in Image Ensembles. International Journal of Computer Vision, Springer Verlag, 2013, 101 (1), pp.161-183. ⟨10.1007/s11263-012-0556-1⟩. ⟨hal-00817565⟩

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