NIELSEN , Mads

Computational anatomy is an emerging discipline at the interface of geometry, statistics and image analysis which aims at modeling and analyzing the biological shape of tissues and organs. The goal is to estimate representative organ anatomies across diseases, populations, species or ages, to model the organ development across time (growth or...

Regularization is often applied to the ill-posed problem of surface reconstruction. This implies the incorporation of a priori knowledge in the solution. The reconstruction depends strongly on this a priori information. Typically, qualitative a priori information is used, leaving it to the user to estimate parameters, (eg. the weak string...

Noise-corrupted signals and images can be reconstructed by minimization of a Hamiltonian. Often the Hamiltonian is a non-convex functional. The solution of minimum energy can then be approximated by the Graduated Non-Convexity (GNC) algorithm developed for the weak membrane by Blake and Zisserman. The GNC approximates the non-convex solution...

n this paper, a generating equation for optic flow is proposed that generalises Horn and Schunck's Optic Flow Constraint Equation (OFCE). Whereas the OFCE has an interpretation as a pointwise conservation law, requiring grey-values associated with fixed-scale volume elements to be constant when co-moving with the flow, the new one can be...

Computational vision often needs to deal with derivatives of digital images. Derivatives are not intrinsic properties of a digital image; a paradigm is required to make them well-defined. Normally, a linear filtering is applied. This can be formulated in terms of scale space, functional minimization or edge detection filters. In this paper, we...

International audience

In order to detect small-scale deformations during disease propagation while allowing large-scale deformation needed for inter-subject registration, we wish to model deformation at multiple scales and represent the deformation at the relevant scales only. With the LDDMM registration framework, enforcing sparsity results in compact...

The Large Deformation Diffeomorphic Metric Mapping frame- work constitutes a widely used and mathematically well-founded setup for registration in medical imaging. At its heart lies the notion of the regularization kernel, and the choice of kernel greatly affects the results of registrations. This paper presents an extension of the LDDMM frame-...

To achieve sparse parametrizations that allow intuitive analysis, we aim to represent deformation with a basis containing interpretable elements, and we wish to use elements that have the description capacity to represent the deformation compactly. To accomplish this, we introduce in this paper higher-order momentum distributions in the large...

In order to detect small-scale deformations during disease propagation while allowing large-scale deformation needed for inter-subject registration, we wish to model deformation at multiple scales and represent the deformation compactly at the relevant scales only. This paper presents the kernel bundle extension of the LDDMM framework that...

In the LDDMM framework, optimal warps for image registration are found as end-points of critical paths for an energy functional, and the EPDiff equations describe the evolution along such paths. The Large Deformation Diffeomorphic Kernel Bundle Mapping (LDDKBM) extension of LDDMM allows scale space information to be automatically incorporated...

Computational anatomy is an emerging discipline at the interface of geometry, statistics and image analysis which aims at modeling and analyzing the biological shape of tissues and organs. The goal is to estimate representative organ anatomies across diseases, populations, species or ages, to model the organ development across time (growth or...

Computational anatomy is an emerging discipline at the interface of geometry, statistics and image analysis which aims at modeling and analyzing the biological shape of tissues and organs. The goal is to estimate representative organ anatomies across diseases, populations, species or ages, to model the organ development across time (growth or...

This book constitutes the refereed joint proceedings of the 4th International Workshop on Multimodal Brain Image Analysis, MBAI 2019, and the 7th International Workshop on Mathematical Foundations of Computational Anatomy, MFCA 2019, held in conjunction with the 22nd International Conference on Medical Imaging and Computer-Assisted...