Journal ArticleDOI
Unifying framework for multimodal brain MRI segmentation based on Hidden Markov Chains.
TLDR
A unifying framework for unsupervised segmentation of multimodal brain MR images including partial volume effect, bias field correction, and information given by a probabilistic atlas is presented.About:
This article is published in Medical Image Analysis.The article was published on 2008-12-01. It has received 89 citations till now. The article focuses on the topics: Brain atlas & Scale-space segmentation.read more
Citations
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Journal ArticleDOI
A review of atlas-based segmentation for magnetic resonance brain images
TL;DR: This paper presents a review of the automated approaches for atlas-based segmentation of magnetic resonance brain images, and aims to point out the strengths and weaknesses of at Atlas-based methods and suggest new research directions.
Journal ArticleDOI
Spatial decision forests for MS lesion segmentation in multi-channel magnetic resonance images.
Ezequiel Geremia,Olivier Clatz,Bjoern H. Menze,Ender Konukoglu,Antonio Criminisi,Nicholas Ayache +5 more
TL;DR: In an a posteriori analysis, it is shown how selected features during classification can be ranked according to their discriminative power and reveal the most important ones.
Journal ArticleDOI
A non-local fuzzy segmentation method: Application to brain MRI
TL;DR: Experiments performed on both synthetic and real MRI data, leading to the classification of brain tissues into grey matter, white matter and cerebrospinal fluid, indicate a significant improvement in performance in the case of higher noise levels, when compared to a range of standard algorithms.
Journal ArticleDOI
Robust Student's-t Mixture Model With Spatial Constraints and Its Application in Medical Image Segmentation
TL;DR: A new finite Student's-t mixture model (SMM) is proposed that exploits Dirichlet distribution andDirichlet law to incorporate the local spatial constrains in an image and is successfully compared to the state-of-the-art finite mixture models.
Journal ArticleDOI
Medical image analysis using wavelet transform and deep belief networks
TL;DR: Results show that the proposed approach can be implemented in real practice for analysing noisy radiography images, which have many useful medical applications such as diagnosis of diseases related to lung, breast, musculoskeletal or pediatric studies.
References
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Journal ArticleDOI
Maximum likelihood from incomplete data via the EM algorithm
Journal ArticleDOI
Equation of state calculations by fast computing machines
TL;DR: In this article, a modified Monte Carlo integration over configuration space is used to investigate the properties of a two-dimensional rigid-sphere system with a set of interacting individual molecules, and the results are compared to free volume equations of state and a four-term virial coefficient expansion.
Journal ArticleDOI
Stochastic Relaxation, Gibbs Distributions, and the Bayesian Restoration of Images
Stuart Geman,Donald Geman +1 more
TL;DR: The analogy between images and statistical mechanics systems is made and the analogous operation under the posterior distribution yields the maximum a posteriori (MAP) estimate of the image given the degraded observations, creating a highly parallel ``relaxation'' algorithm for MAP estimation.
Book
Bayesian Data Analysis
TL;DR: Detailed notes on Bayesian Computation Basics of Markov Chain Simulation, Regression Models, and Asymptotic Theorems are provided.
Journal ArticleDOI
Fast robust automated brain extraction
TL;DR: An automated method for segmenting magnetic resonance head images into brain and non‐brain has been developed and described and examples of results and the results of extensive quantitative testing against “gold‐standard” hand segmentations, and two other popular automated methods.