Spatially regularized mixture model for lesion segmentation with application to stroke patients.

No abstract is available for this paper.

• Publication year

  2015

• Venue

  Biostatistics

• Publication date

  2015-07-01

• Fields of study

  Medicine, Computer Science

• Identifiers
  DOI 10.1093/biostatistics/kxv004PMID 25745872
• External record

  Open on Semantic Scholar

• Source metadata

  Semantic Scholar, PubMed

• No claims are published for this paper.

• No concepts are published for this paper.

• OASIS is Automated Statistical Inference for Segmentation, with applications to multiple sclerosis lesion segmentation in MRI

  2013cited by this paper
• FLAIR lesion segmentation: application in patients with brain tumors and acute ischemic stroke.

  2013cited by this paper
• MRI Tissue Classification Using High-Resolution Bayesian Hidden Markov Normal Mixture Models

  2012cited by this paper
• Estimating the Granularity Coefficient of a Potts-Markov Random Field Within a Markov Chain Monte Carlo Algorithm

  2012cited by this paper
• A comparison of different automated methods for the detection of white matter lesions in MRI data

  2011cited by this paper
• Spatial Statistics and Modeling

  2009cited by this paper
• Misclassified Tissue Volumes in Alzheimer Disease Patients With White Matter Hyperintensities: Importance of Lesion Segmentation Procedures for Volumetric Analysis

  2008cited by this paper
• On evaluating brain tissue classifiers without a ground truth

  2007cited by this paper
• Multimodal MRI segmentation of ischemic stroke lesions

  2007cited by this paper
• Probabilistic segmentation and intensity estimation for microarray images.

  2006cited by this paper
• Convergence Theorems for Generalized Alternating Minimization Procedures

  2005cited by this paper
• Mixture models with adaptive spatial regularization for segmentation with an application to FMRI data

  2005cited by this paper
• Fully automatic segmentation of white matter hyperintensities in MR images of the elderly

  2005cited by this paper
• Nonlinear Diffusion Scale-Space and Fast Marching Level Sets for Segmentation of MR Imagery and Volume Estimation of Stroke Lesions

  2003cited by this paper
• Segmentation of brain MR images through a hidden Markov random field model and the expectation-maximization algorithm

  2001cited by this paper
• An Introduction to Variational Methods for Graphical Models

  1999cited by this paper
• A new minimum variance region growing algorithm for image segmentation

  1997cited by this paper
• Magnetic Resonance Abnormalities and Cardiovascular Disease in Older Adults: The Cardiovascular Health Study

  1994cited by this paper
• the Mean Field Theory in EM Procedures for Markov Random Fields

  1991cited by this paper
• Concomitant-Variable Latent-Class Models

  1988cited by this paper
• Spatial Interaction and the Statistical Analysis of Lattice Systems

  1974cited by this paper

• Optimizing convex hull discovery: Introducing a quintuple-region algorithm with enhanced computational efficiency

  2025cites this paper
• Characterization of a robust probabilistic framework for brain magnetic resonance image data distributions

  2023cites this paper
• Automatic brain lesion segmentation on standard magnetic resonance images: a scoping review

  2021cites this paper
• Minimax Estimation of the Volume of a Set Under the Rolling Ball Condition

  2018cites this paper
• Population based spatio-temporal probabilistic modelling of fMRI data

  2018cites this paper
• Statistical methods for weather-related insurance claims

  2017cites this paper
• Robust generative asymmetric GMM for brain MR image segmentation

  2017cites this paper
• Lesion identification and the effect of lesion on motor mapping after stroke

  2016cites this paper
• Automated segmentation of chronic stroke lesions using LINDA: Lesion identification with neighborhood data analysis

  2016cites this paper