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Open AccessJournal ArticleDOI

Automatic detection and segmentation of evolving processes in 3D medical images: Application to multiple sclerosis.

TLDR
The objective of this work was to automatically detect regions with apparent local volume variation with a vector field operator applied to the local displacement field obtained after a non-rigid registration between two successive temporal images.
About
This article is published in Medical Image Analysis.The article was published on 2002-06-01 and is currently open access. It has received 244 citations till now. The article focuses on the topics: Segmentation.

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Citations
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Medical image analysis: progress over two decades and the challenges ahead

TL;DR: A look at progress in the field over the last 20 years is looked at and some of the challenges that remain for the years to come are suggested.

Image change detectio algorithms : A systematic survey

R. J. Radke
TL;DR: A systematic survey of the common processing steps and core decision rules in modern change detection algorithms, including significance and hypothesis testing, predictive models, the shading model, and background modeling is presented.
Journal ArticleDOI

Image change detection algorithms: a systematic survey

TL;DR: In this paper, the authors present a systematic survey of the common processing steps and core decision rules in modern change detection algorithms, including significance and hypothesis testing, predictive models, the shading model, and background modeling.
Journal ArticleDOI

A Riemannian Framework for Tensor Computing

TL;DR: This paper proposes to endow the tensor space with an affine-invariant Riemannian metric and demonstrates that it leads to strong theoretical properties: the cone of positive definite symmetric matrices is replaced by a regular and complete manifold without boundaries, the geodesic between two tensors and the mean of a set of tensors are uniquely defined.
Journal ArticleDOI

Automated segmentation of multiple sclerosis lesions by model outlier detection

TL;DR: A fully automated algorithm for segmentation of multiple sclerosis lesions from multispectral magnetic resonance (MR) images that performs intensity-based tissue classification using a stochastic model and simultaneously detects MS lesions as outliers that are not well explained by the model.
References
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Journal ArticleDOI

Multimodality image registration by maximization of mutual information

TL;DR: The results demonstrate that subvoxel accuracy with respect to the stereotactic reference solution can be achieved completely automatically and without any prior segmentation, feature extraction, or other preprocessing steps which makes this method very well suited for clinical applications.
Journal ArticleDOI

A survey of image registration techniques

TL;DR: This paper organizes this material by establishing the relationship between the variations in the images and the type of registration techniques which can most appropriately be applied, and establishing a framework for understanding the merits and relationships between the wide variety of existing techniques.
Journal ArticleDOI

Image matching as a diffusion process: an analogy with Maxwell's demons

TL;DR: The main idea is to consider the objects boundaries in one image as semi-permeable membranes and to let the other image, considered as a deformable grid model, diffuse through these interfaces, by the action of effectors situated within the membranes.
Journal ArticleDOI

Finite element implementation of incompressible, transversely isotropic hyperelasticity

TL;DR: In this article, a three-dimensional constitutive model for biological soft tissues and its finite element implementation for fully incompressible material behavior is presented, along with derivations of the stress and elasticity tensors for a transversely isotropic, hyperelastic material.
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