Markov Random Field Segmentation of Brain MR Images
Karsten Held,Elena Rota Kops,Bernd J. Krause,William M. Wells,Ron Kikinis,Hans-Wilhelm Mueller-Gaertner +5 more
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TLDR
Describes a fully-automatic three-dimensional (3-D)-segmentation technique for brain magnetic resonance (MR) images that captures three features that are of special importance for MR images, i.e., nonparametric distributions of tissue intensities, neighborhood correlations, and signal inhomogeneities.Abstract:
We describe a fully-automatic 3D-segmentation technique for brain MR images. Using Markov random fields the segmentation algorithm captures three important MR features, i.e. non-parametric distributions of tissue intensities, neighborhood correlations and signal inhomogeneities. Detailed simulations and real MR images demonstrate the performance of the segmentation algorithm. The impact of noise, inhomogeneity, smoothing and structure thickness is analyzed quantitatively. Even single echo MR images are well classified into gray matter, white matter, cerebrospinal fluid, scalp-bone and background. A simulated annealing and an iterated conditional modes implementation are presented.
Keywords: Magnetic Resonance Imaging, Segmentation, Markov Random Fieldsread more
Citations
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Segmentation of brain MR images through a hidden Markov random field model and the expectation-maximization algorithm
TL;DR: The authors propose a novel hidden Markov random field (HMRF) model, which is a stochastic process generated by a MRF whose state sequence cannot be observed directly but which can be indirectly estimated through observations.
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Automatically Parcellating the Human Cerebral Cortex
Bruce Fischl,Andre van der Kouwe,Christophe Destrieux,Eric Halgren,Florent Ségonne,David H. Salat,Evelina Busa,Larry J. Seidman,Jill M. Goldstein,David N. Kennedy,Verne S. Caviness,Nikos Makris,Bruce R. Rosen,Anders M. Dale +13 more
TL;DR: A technique for automatically assigning a neuroanatomical label to each location on a cortical surface model based on probabilistic information estimated from a manually labeled training set is presented, comparable in accuracy to manual labeling.
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Current methods in medical image segmentation.
TL;DR: A critical appraisal of the current status of semi-automated and automated methods for the segmentation of anatomical medical images is presented, with an emphasis on the advantages and disadvantages of these methods for medical imaging applications.
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Automated model-based tissue classification of MR images of the brain
TL;DR: The algorithm is able to segment single- and multi-spectral MR images, corrects for MR signal inhomogeneities, and incorporates contextual information by means of Markov random Fields (MRF's).
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Automated model-based bias field correction of MR images of the brain
TL;DR: The method the authors propose applies an iterative expectation-maximization (EM) strategy that interleaves pixel classification with estimation of class distribution and bias field parameters, improving the likelihood of the model parameters at each iteration.
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