The problems of segmentation and registration are traditionally approached individually, yet the accuracy of one is of great importance in influencing the success of the other. In this paper, we aim to show that more accurate and robust results may be obtained through seeking a joint solution to these linked processes. The outlined approach applies Markov random fields in the solution of a maximum a posteriori model of segmentation and registration. The approach is applied to synthetic and real MRI data
International audienceThe problem of jointly segmenting objects, according to a set of labels (of ca...
In this thesis, we propose a novel framework for knowledge-based segmentation using high-order Marko...
International audienceMarkov Random Fields in Image Segmentation provides an introduction to the fun...
Markov random field (MRF) theory has widely been applied to segmentation in noisy images. This paper...
Markov random field (MRF) theory has widely been applied to segmentation in noisy images. This paper...
Markov random field (MRF) theory has widely been applied to segmentation in noisy images. This paper...
International audienceMulti-atlas segmentation has emerged in recent years as a simple yet powerful ...
International audienceWe consider a general modelling strategy to handle in a unified way a number o...
Abstract. The paper introduces an algorithm which allows the automatic segmentation of multi channel...
International audienceRigid slice-to-volume registration is a challenging task, which finds applicat...
We present a statistical framework that combines the registration of an atlas with the segmentation ...
International audienceOBJECTIVE: Markov random field (MRF) models have been traditionally applied to...
International audienceThe problem of jointly segmenting objects, according to a set of labels (of ca...
In this thesis, we propose a novel framework for knowledge-based segmentation using high-order Marko...
International audienceMarkov Random Fields in Image Segmentation provides an introduction to the fun...
Markov random field (MRF) theory has widely been applied to segmentation in noisy images. This paper...
Markov random field (MRF) theory has widely been applied to segmentation in noisy images. This paper...
Markov random field (MRF) theory has widely been applied to segmentation in noisy images. This paper...
International audienceMulti-atlas segmentation has emerged in recent years as a simple yet powerful ...
International audienceWe consider a general modelling strategy to handle in a unified way a number o...
Abstract. The paper introduces an algorithm which allows the automatic segmentation of multi channel...
International audienceRigid slice-to-volume registration is a challenging task, which finds applicat...
We present a statistical framework that combines the registration of an atlas with the segmentation ...
International audienceOBJECTIVE: Markov random field (MRF) models have been traditionally applied to...
International audienceThe problem of jointly segmenting objects, according to a set of labels (of ca...
In this thesis, we propose a novel framework for knowledge-based segmentation using high-order Marko...
International audienceMarkov Random Fields in Image Segmentation provides an introduction to the fun...