Cargando…

PET image segmentation using a Gaussian mixture model and Markov random fields

BACKGROUND: Classification algorithms for positron emission tomography (PET) images support computational treatment planning in radiotherapy. Common clinical practice is based on manual delineation and fixed or iterative threshold methods, the latter of which requires regression curves dependent on...

Descripción completa

Detalles Bibliográficos
Autores principales:	Layer, Thomas, Blaickner, Matthias, Knäusl, Barbara, Georg, Dietmar, Neuwirth, Johannes, Baum, Richard P, Schuchardt, Christiane, Wiessalla, Stefan, Matz, Gerald
Formato:	Online Artículo Texto
Lenguaje:	English
Publicado:	Springer International Publishing 2015
Materias:	Research Article
Acceso en línea:	https://www.ncbi.nlm.nih.gov/pmc/articles/PMC4545759/ https://www.ncbi.nlm.nih.gov/pubmed/26501811 http://dx.doi.org/10.1186/s40658-015-0110-7

Ejemplares similares

Gaussian Markov random fields: theory and applications
por: Rue, Havard, et al.
Publicado: (2005)

Approximating Hidden Gaussian Markov Random Fields
por: Rue, H, et al.
Publicado: (2003)

Marginal Variances for Gaussian Markov Random Fields
por: Rue, H
Publicado: (2005)

Fast sampling of Gaussian Markov random fields with applications
por: Rue, H
Publicado: (2000)

Preprocessing of (18)F-DMFP-PET Data Based on Hidden Markov Random Fields and the Gaussian Distribution
por: Segovia, Fermín, et al.
Publicado: (2017)

Segmenting Continuous Motions with Hidden Semi-markov Models and Gaussian Processes
por: Nakamura, Tomoaki, et al.
Publicado: (2017)

Gaussian Markov Random Field Models With Applications in Spatial Statistics
por: Rue, H, et al.
Publicado: (2003)

Overlapping block proposals for latent Gaussian Markov random fields
por: Steinsland, I, et al.
Publicado: (2003)

Approximate Inference for Hierarchical Gaussian Markov Random Fields Models
por: Rue, H, et al.
Publicado: (2005)

Revisiting Gaussian Markov random fields and Bayesian disease mapping
por: MacNab, Ying C
Publicado: (2022)

Modelling spatial variation in disease risk using Gaussian Markov random field proxies for Gaussian random fields
por: Follestad, T, et al.
Publicado: (2003)

Supervoxel Segmentation with Voxel-Related Gaussian Mixture Model
por: Ban, Zhihua, et al.
Publicado: (2018)

Modelling state‐transition dynamics in resting‐state brain signals by the hidden Markov and Gaussian mixture models
por: Ezaki, Takahiro, et al.
Publicado: (2021)

Specifying a Gaussian Markov random field by a sparse Cholesky triangle
por: Wist, H T, et al.
Publicado: (2005)

Markov processes, Gaussian processes, and local times
por: Marcus, Michael B, et al.
Publicado: (2006)

Multiparametric brainstem segmentation using a modified multivariate mixture of Gaussians()
por: Lambert, Christian, et al.
Publicado: (2013)

GMRFLib: a C-library for fast and exact simulation of Gaussian Markov random fields
por: Rue, H, et al.
Publicado: (2001)

Adaptive Gaussian Markov random field spatiotemporal models for infectious disease mapping and forecasting
por: MacNab, Ying C.
Publicado: (2023)

Wavelet-based Gaussian-mixture hidden Markov model for the detection of multistage seizure dynamics: A proof-of-concept study
por: Chiu, Alan WL, et al.
Publicado: (2011)

Making Inference from Bayesian Animal Models utilising Gaussian Markov Random Field properties
por: Steinsland, Ingelin, et al.
Publicado: (2005)

Automatic Lung Tumor Segmentation on PET/CT Images Using Fuzzy Markov Random Field Model
por: Guo, Yu, et al.
Publicado: (2014)

Multimodal Brain-Tumor Segmentation Based on Dirichlet Process Mixture Model with Anisotropic Diffusion and Markov Random Field Prior
por: Lu, Yisu, et al.
Publicado: (2014)

Multigrid Nonlocal Gaussian Mixture Model for Segmentation of Brain Tissues in Magnetic Resonance Images
por: Chen, Yunjie, et al.
Publicado: (2016)

Human Activity Recognition Using Gaussian Mixture Hidden Conditional Random Fields
por: Siddiqi, Muhammad Hameed, et al.
Publicado: (2019)

Spike sorting with Gaussian mixture models
por: Souza, Bryan C., et al.
Publicado: (2019)

A Rough Set Bounded Spatially Constrained Asymmetric Gaussian Mixture Model for Image Segmentation
por: Ji, Zexuan, et al.
Publicado: (2017)

Research on a Segmentation Algorithm for the Tujia Brocade Images Based on Unsupervised Gaussian Mixture Clustering
por: He, Shuqi
Publicado: (2021)

Finite mixture and Markov switching models
por: Frühwirth-Schnatter, Sylvia
Publicado: (2006)

Fully Bayesian Prediction Algorithms for Mobile Robotic Sensors under Uncertain Localization Using Gaussian Markov Random Fields
por: Jadaliha, Mahdi, et al.
Publicado: (2018)

Mixtures of Conditional Gaussian Scale Mixtures Applied to Multiscale Image Representations
por: Theis, Lucas, et al.
Publicado: (2012)

Markov random field segmentation for industrial computed tomography with metal artefacts
por: Jaiswal, Avinash, et al.
Publicado: (2018)

Gaussian Mixture Model of Heart Rate Variability
por: Costa, Tommaso, et al.
Publicado: (2012)

A Fast Incremental Gaussian Mixture Model
por: Pinto, Rafael Coimbra, et al.
Publicado: (2015)

Fitting Gaussian mixture models on incomplete data
por: McCaw, Zachary R., et al.
Publicado: (2022)

The impact of ordinal scales on Gaussian mixture recovery
por: Haslbeck, Jonas M. B., et al.
Publicado: (2022)

Data on MRI brain lesion segmentation using K-means and Gaussian Mixture Model-Expectation Maximization
por: Qiao, Ju, et al.
Publicado: (2019)

A New Markov Random Field Segmentation Method for Breast Lesion Segmentation in MR images
por: Azmi, Reza, et al.
Publicado: (2011)

An Automatic Algorithm for Segmentation of the Boundaries of Corneal Layers in Optical Coherence Tomography Images using Gaussian Mixture Model
por: Jahromi, Mahdi Kazemian, et al.
Publicado: (2014)

Dempster-Shafer Parzen-Rosenblatt Hidden Markov Fields for Multichannel Image Segmentation
por: Boudaren, Mohamed El Yazid, et al.
Publicado: (2020)

Correction: A Fast Incremental Gaussian Mixture Model
por: Pinto, Rafael Coimbra, et al.
Publicado: (2015)

Cannot write session to /tmp/vufind_sessions/sess_6g9d1682551g5kcqv0vq5m0dok