In this paper, a novel spatial feature, namely maximum distance-gradient-magnitude (MDGM), is defined for registration tasks. For each voxel in an image, the MDGM feature encodes s...
Abstract. This paper presents a novel approach for object segmentation in medical images that respects the topological relationships of multiple structures as given by a template. ...
Modeling the variability of brain structures is a fundamental problem in the neurosciences. In this paper, we start from a dataset of precisely delineated anatomical structures in ...
Pierre Fillard, Vincent Arsigny, Xavier Pennec, Pa...
Here we model the effect of non-overlapping voxels on image registration, and show that a major defect of overlap-only models--their limited capture range--can be alleviated. Theor...
In this paper, we study Markov Random Fields as spatial smoothing priors in fMRI detection. Relatively high noise in fMRI images presents a serious challenge for the detection algo...
This paper presents a novel non-rigid registration method. The main contribution of the method is the modeling of the vorticity (respectively divergence) of the deformation field u...
In this paper we develop a novel measure of information in a random variable based on its cumulative distribution that we dub cumulative residual entropy (CRE). This measure parall...
We formulate and interpret several registration methods in the context of a unified statistical and information theoretic framework. A unified interpretation clarifies the implici...