Cited by 7,507Medical Image Segmentation TechniquesBrain Tumor Detection and ClassificationAdvanced MRI Techniques and Applications

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Unified segmentation

Ashburner, John, Friston, Karl J.·2005·NeuroImage

DOI 10.1016/j.neuroimage.2005.02.018

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Abstract

A probabilistic framework is presented that enables image registration, tissue classification, and bias correction to be combined within the same generative model. A derivation of a log-likelihood objective function for the unified model is provided. The model is based on a mixture of Gaussians and is extended to incorporate a smooth intensity variation and nonlinear registration with tissue probability maps. A strategy for optimising the model parameters is described, along with the requisite partial derivatives of the objective function.

Integrity signals

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Crossref · 10.1016/j.neuroimage.2005.02.018

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Authors: Ashburner, J., & Friston, K. J.
Affiliations: National Hospital for Neurology and Neurosurgery (GB)
Corresponding author: Ashburner, John
ORCID: Ashburner, John
Friston, Karl J.
Venue: NeuroImage
Publisher: Elsevier BV
Published: 2005-07
Volume: 26
Issue: 3
Pages: 839-851
ISSN: 1053-8119 (ISSN-L)
DOI: 10.1016/j.neuroimage.2005.02.018
OpenAlex: W4230920194
Cited by: 7,507
References: 59
Citations by year: 2026: 314 · 2025: 414 · 2024: 450 · 2023: 386 · 2022: 460 · 2021: 507 · 2020: 479 · 2019: 449 · 2018: 434 · 2017: 442 · 2016: 445 · 2015: 458 · 2014: 441 · 2013: 454 · 2012: 380
FWCI: 81.00
Type: journal article
Language: en
Open access: closed
Licence: www.elsevier.com/tdm/userlicense/1.0/ (tdm)
www.elsevier.com/legal/tdmrep-license (tdm)
doi.org/10.15223/policy-017 (stm-asf)
doi.org/10.15223/policy-037 (stm-asf)
doi.org/10.15223/policy-012 (stm-asf)
doi.org/10.15223/policy-029 (stm-asf)
doi.org/10.15223/policy-004 (stm-asf)
Locations: NeuroImage (publishedVersion)
PubMed (publishedVersion)
UCL Discovery (University College London) (submittedVersion)
Funders: Wellcome Trust
Topics: Physical Sciences › Computer Science › Computer Vision and Pattern Recognition › Medical Image Segmentation Techniques
Physical Sciences › Computer Science › Computer Vision and Pattern Recognition › Brain Tumor Detection and Classification
Health Sciences › Medicine › Radiology, Nuclear Medicine and Imaging › Advanced MRI Techniques and Applications
Keywords: probabilistic segmentation, image registration, tissue classification, bias correction, Gaussian mixture model, nonlinear registration, tissue probability maps, parameter optimization
MeSH terms: Algorithms, Brain Mapping, Data Interpretation, Statistical, Image Processing, Computer-Assisted, Magnetic Resonance Imaging, Models, Neurological, Probability Theory, Models, Statistical, Normal Distribution, Likelihood Functions, Fuzzy Logic, Nonlinear Dynamics

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