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Isabelle Bloch

Bio: Isabelle Bloch is an academic researcher from Télécom ParisTech. The author has contributed to research in topics: Fuzzy set & Segmentation. The author has an hindex of 50, co-authored 572 publications receiving 13056 citations. Previous affiliations of Isabelle Bloch include Institut Mines-Télécom & French Institute of Health and Medical Research.


Papers
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Journal ArticleDOI
TL;DR: This paper reviews state-of-the-art literature on vascular segmentation with a particular focus on 3D contrast-enhanced imaging modalities (MRA and CTA) and discusses the theoretical and practical properties of recent approaches and highlight the most advanced and promising ones.

951 citations

Journal ArticleDOI
01 Jan 1996
TL;DR: A classification of operators issued from the different data fusion theories with respect to their behavior provides a guide for choosing an operator in a given problem and can be refined from the desired properties of the operators, from their decisiveness, and by examining how they deal with conflictive situations.
Abstract: In most data fusion systems, the information extracted from each sensor (either numerical or symbolic) is represented as a degree of belief in an event with real values, taking in this way into account the imprecise, uncertain, and incomplete nature of the information. The combination of such degrees of belief is performed through numerical fusion operators. A very large variety of such operators has been proposed in the literature. We propose in this paper a classification of these operators issued from the different data fusion theories with respect to their behavior. Three classes are thus defined. This classification provides a guide for choosing an operator in a given problem. This choice can then be refined from the desired properties of the operators, from their decisiveness, and by examining how they deal with conflictive situations.

719 citations

Journal ArticleDOI
TL;DR: A study of the tracking behavior according to the influence given to the a priori knowledge is proposed and concrete tracking results obtained with in vivo human brain data are illustrated.

428 citations

Journal ArticleDOI
TL;DR: The purpose of this paper is to present and discuss the different ways to build a fuzzy mathematical morphology, and compare their properties with respect to mathematical morphology and to fuzzy sets and interpret them in terms of logic and decision theory.

384 citations

Journal ArticleDOI
TL;DR: An algorithm allowing the construction of a structural representation of the cortical topography from a T1-weighted 3D MR image and an attributed relational graph (ARG) inferred from the 3D skeleton of the object made up of the union of gray matter and cerebro-spinal fluid enclosed in the brain hull is proposed.
Abstract: We propose an algorithm allowing the construction of a structural representation of the cortical topography from a T1-weighted 3D MR image. This representation is an attributed relational graph (ARG) inferred from the 3D skeleton of the object made up of the union of gray matter and cerebro-spinal fluid enclosed in the brain hull. In order to increase the robustness of the skeletonization, topological and regularization constraints are included in the segmentation process using an original method: the homotopically deformable regions. This method is halfway between deformable contour and Markovian segmentation approaches. The 3D skeleton is segmented in simple surfaces (SSs) constituting the ARG nodes (mainly cortical folds). The ARG relations are of two types: first, theSS pairs connected in the skeleton; second, theSS pairs delimiting a gyrus. The described algorithm has been developed in the frame of a project aiming at the automatic detection and recognition of the main cortical sulci. Indeed, the ARG is a synthetic representation of all the information required by the sulcus identification. This project will contribute to the development of new methodologies for human brain functional mapping and neurosurgery operation planning.

372 citations


Cited by
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Christopher M. Bishop1
01 Jan 2006
TL;DR: Probability distributions of linear models for regression and classification are given in this article, along with a discussion of combining models and combining models in the context of machine learning and classification.
Abstract: Probability Distributions.- Linear Models for Regression.- Linear Models for Classification.- Neural Networks.- Kernel Methods.- Sparse Kernel Machines.- Graphical Models.- Mixture Models and EM.- Approximate Inference.- Sampling Methods.- Continuous Latent Variables.- Sequential Data.- Combining Models.

10,141 citations

Journal ArticleDOI
TL;DR: An automated labeling system for subdividing the human cerebral cortex into standard gyral-based neuroanatomical regions is both anatomically valid and reliable and may be useful for both morphometric and functional studies of the cerebral cortex.

9,940 citations

01 Jan 2002

9,314 citations

Journal ArticleDOI
TL;DR: A new method is proposed which attempts to keep the sensitivity benefits of cluster-based thresholding (and indeed the general concept of "clusters" of signal), while avoiding (or at least minimising) these problems, and is referred to as "threshold-free cluster enhancement" (TFCE).

4,466 citations