LOCUS: LOcal Cooperative Unified Segmentation of
MRI Brain Scans 1st Edition by B Scherrer, M
Dojat, F Forbes, C Garbay ISBN 9783540757573 pdf
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Comparative Dental Morphology 1st Edition by T Koppe, G Meyer, K W
Alt, A Brook, M C Dean, I Kjaer, J R Lukacs, B H Smith, M F Teaford
ISBN B06Y6FC5FZ 9783805592291
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Forensic Medicine Fundamentals and Perspectives 1st Edition by
Reinhard B Dettmeyer, Marcel A Verhoff, Harald F Schutz ISBN
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Effects of Registration Regularization and Atlas Sharpness on
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Rahul Desikan, Bruce Fischl, Polina Golland ISBN 9783540757573
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, LOCUS: LOcal Cooperative Unified
Segmentation of MRI Brain Scans
B. Scherrer1,2 , M. Dojat1 , F. Forbes3 , and C. Garbay2
1
INSERM U836-UJF-CEA-CHU (Grenoble Institute of Neuroscience)
2
LIG (Laboratoire d’Informatique de Grenoble), CNRS UMR 5217 (MAGMA)
3
INRIA, Laboratoire Jean Kuntzmann, Universite de Grenoble (MISTIS)
Abstract. We propose to carry out cooperatively both tissue and struc-
ture segmentations by distributing a set of local and cooperative models
in a unified MRF framework. Tissue segmentation is performed by par-
titionning the volume into subvolumes where local MRFs are estimated
in cooperation with their neighbors to ensure consistency. Local estima-
tion fits precisely to the local intensity distribution and thus handles
nonuniformity of intensity without any bias field modelization. Struc-
ture segmentation is performed via local MRFs that integrate localiza-
tion constraints provided by a priori general fuzzy description of brain
anatomy. Structure segmentation is not reduced to a postprocessing step
but cooperates with tissue segmentation to gradually and conjointly im-
prove models accuracy. The evaluation was performed using phantoms
and real 3T brain scans. It shows good results and in particular robust-
ness to nonuniformity and noise with a low computational cost.
1 Introduction
MRI brain scan segmentation is a challenging task and has been widely addressed
in the last 15 years. Difficulties in automatic segmentation arise from various
sources including the size of the data, the low contrast between tissues, the lim-
itations of available a priori knowledge, local perturbations such as noise or
global perturbations such as intensity nonuniformity. Current approaches share
three main characteristics: first, tissue and structure segmentations are consid-
ered as two separate tasks whereas they are clearly linked. Second, for a robust to
noise segmentation, the Markov Random Field (MRF) probabilistic framework
is classically used to introduce spatial dependencies between voxels [1,2]. Third,
tissue models are generally estimated globally through the entire volume and do
not reflect spatial intensity variations within each tissue, due mainly to biological
tissue properties and to MRI hardware imperfections. Only the latter is generally
addressed, modeled by the introduction of an explicit so called “bias field” model
to estimate. Local segmentation is an attractive alternative. The principle is to
compute models in various subvolumes to fit better to local image properties.
However, the few local approaches proposed to date are clearly limited: they use
local estimation as a preprocessing step only to estimate a bias field model [3],
a training set for statistical local shape modelling [4], redondant information to
N. Ayache, S. Ourselin, A. Maeder (Eds.): MICCAI 2007, Part I, LNCS 4791, pp. 219–227, 2007.
c Springer-Verlag Berlin Heidelberg 2007
MRI Brain Scans 1st Edition by B Scherrer, M
Dojat, F Forbes, C Garbay ISBN 9783540757573 pdf
download
https://ebookball.com/product/locus-local-cooperative-unified-
segmentation-of-mri-brain-scans-1st-edition-by-b-scherrer-m-
dojat-f-forbes-c-garbay-isbn-9783540757573-13380/
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, Get Your Digital Files Instantly: PDF, ePub, MOBI and More
Quick Digital Downloads: PDF, ePub, MOBI and Other Formats
Comparative Dental Morphology 1st Edition by T Koppe, G Meyer, K W
Alt, A Brook, M C Dean, I Kjaer, J R Lukacs, B H Smith, M F Teaford
ISBN B06Y6FC5FZ 9783805592291
https://ebookball.com/product/comparative-dental-morphology-1st-
edition-by-t-koppe-g-meyer-k-w-alt-a-brook-m-c-dean-i-kjaer-j-r-
lukacs-b-h-smith-m-f-teaford-isbn-b06y6fc5fz-9783805592291-7282/
Graph Cuts Framework for Kidney Segmentation with Prior Shape
Constraints 1st Edition by Asem M Ali, Aly A Farag, Ayman S El Baz
ISBN 9783540757573
https://ebookball.com/product/graph-cuts-framework-for-kidney-
segmentation-with-prior-shape-constraints-1st-edition-by-asem-m-
ali-aly-a-farag-ayman-s-el-baz-isbn-9783540757573-13514/
Registration of High Angular Resolution Diffusion MRI Images Using 4th
Order Tensors 1st Edition by Angelos Barmpoutis, Baba C VemurI, John R
Forder ISBN 9783540757573
https://ebookball.com/product/registration-of-high-angular-
resolution-diffusion-mri-images-using-4th-order-tensors-1st-
edition-by-angelos-barmpoutis-baba-c-vemuri-john-r-forder-
isbn-9783540757573-13718/
(Ebook PDF) Quantitative MRI of the Brain 1st edition by Mara
Cercignani 1315363550 9781315363554 full chapters
https://ebookball.com/product/ebook-pdf-quantitative-mri-of-the-
brain-1st-edition-by-mara-
cercignani-1315363550-9781315363554-full-chapters-22492/
,Real Time MR Diffusion Tensor and Q Ball Imaging Using Kalman
Filtering 1st Edition by C Poupon, F Poupon, A Roche, Y Cointepas, J
Dubois, JF Mangin ISBN 9783540757573
https://ebookball.com/product/real-time-mr-diffusion-tensor-and-
q-ball-imaging-using-kalman-filtering-1st-edition-by-c-poupon-f-
poupon-a-roche-y-cointepas-j-dubois-jf-mangin-
isbn-9783540757573-12286/
Hyperspherical von Mises Fisher Mixture HvMF Modelling of High Angular
Resolution Diffusion MRI 1st Edition by Abhir Bhalerao, Carl Fredrik
Westin ISBN 9783540757573
https://ebookball.com/product/hyperspherical-von-mises-fisher-
mixture-hvmf-modelling-of-high-angular-resolution-diffusion-
mri-1st-edition-by-abhir-bhalerao-carl-fredrik-westin-
isbn-9783540757573-14452/
Subject Specific Biomechanical Simulation of Brain Indentation Using a
Meshless Method 1st Edition by Ashley Horton, Adam Wittek, Karol
Miller ISBN 9783540757573
https://ebookball.com/product/subject-specific-biomechanical-
simulation-of-brain-indentation-using-a-meshless-method-1st-
edition-by-ashley-horton-adam-wittek-karol-miller-
isbn-9783540757573-13510/
Forensic Medicine Fundamentals and Perspectives 1st Edition by
Reinhard B Dettmeyer, Marcel A Verhoff, Harald F Schutz ISBN
3642388183 9783642388187
https://ebookball.com/product/forensic-medicine-fundamentals-and-
perspectives-1st-edition-by-reinhard-b-dettmeyer-marcel-a-
verhoff-harald-f-schutz-isbn-3642388183-9783642388187-4294/
Effects of Registration Regularization and Atlas Sharpness on
Segmentation Accuracy 1st Edition by BT Thomas Yeo, Mert R Sabuncu,
Rahul Desikan, Bruce Fischl, Polina Golland ISBN 9783540757573
https://ebookball.com/product/effects-of-registration-
regularization-and-atlas-sharpness-on-segmentation-accuracy-1st-
edition-by-bt-thomas-yeo-mert-r-sabuncu-rahul-desikan-bruce-
fischl-polina-golland-isbn-9783540757573-13374/
, LOCUS: LOcal Cooperative Unified
Segmentation of MRI Brain Scans
B. Scherrer1,2 , M. Dojat1 , F. Forbes3 , and C. Garbay2
1
INSERM U836-UJF-CEA-CHU (Grenoble Institute of Neuroscience)
2
LIG (Laboratoire d’Informatique de Grenoble), CNRS UMR 5217 (MAGMA)
3
INRIA, Laboratoire Jean Kuntzmann, Universite de Grenoble (MISTIS)
Abstract. We propose to carry out cooperatively both tissue and struc-
ture segmentations by distributing a set of local and cooperative models
in a unified MRF framework. Tissue segmentation is performed by par-
titionning the volume into subvolumes where local MRFs are estimated
in cooperation with their neighbors to ensure consistency. Local estima-
tion fits precisely to the local intensity distribution and thus handles
nonuniformity of intensity without any bias field modelization. Struc-
ture segmentation is performed via local MRFs that integrate localiza-
tion constraints provided by a priori general fuzzy description of brain
anatomy. Structure segmentation is not reduced to a postprocessing step
but cooperates with tissue segmentation to gradually and conjointly im-
prove models accuracy. The evaluation was performed using phantoms
and real 3T brain scans. It shows good results and in particular robust-
ness to nonuniformity and noise with a low computational cost.
1 Introduction
MRI brain scan segmentation is a challenging task and has been widely addressed
in the last 15 years. Difficulties in automatic segmentation arise from various
sources including the size of the data, the low contrast between tissues, the lim-
itations of available a priori knowledge, local perturbations such as noise or
global perturbations such as intensity nonuniformity. Current approaches share
three main characteristics: first, tissue and structure segmentations are consid-
ered as two separate tasks whereas they are clearly linked. Second, for a robust to
noise segmentation, the Markov Random Field (MRF) probabilistic framework
is classically used to introduce spatial dependencies between voxels [1,2]. Third,
tissue models are generally estimated globally through the entire volume and do
not reflect spatial intensity variations within each tissue, due mainly to biological
tissue properties and to MRI hardware imperfections. Only the latter is generally
addressed, modeled by the introduction of an explicit so called “bias field” model
to estimate. Local segmentation is an attractive alternative. The principle is to
compute models in various subvolumes to fit better to local image properties.
However, the few local approaches proposed to date are clearly limited: they use
local estimation as a preprocessing step only to estimate a bias field model [3],
a training set for statistical local shape modelling [4], redondant information to
N. Ayache, S. Ourselin, A. Maeder (Eds.): MICCAI 2007, Part I, LNCS 4791, pp. 219–227, 2007.
c Springer-Verlag Berlin Heidelberg 2007
