The DOI matches its Crossref record; 59 of the 59 references it cites were found in a registry or an index.
Unified segmentation
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.
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“Studies suggest that tobacco smokers have higher β2*-nAChR availability compared with non-smokers in non-psychiatric populations (34) which normalizes by 6–12 weeks of abstinence (44).”
From Persistent β 2 *-Nicotinic Acetylcholinergic Receptor Dysfunction in Major Depressive Disorder (2012) · cites this paper as [44]
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Metadata
- Authors
- Ashburner, J., & Friston, K. J.
- Affiliations
- National Hospital for Neurology and Neurosurgery (GB)
- Corresponding author
- Ashburner, John
- Venue
- NeuroImage
- Publisher
- Elsevier BV
- Published
- 2005-07
- Volume
- 26
- Issue
- 3
- Pages
- 839-851
- ISSN
- 1053-8119 (ISSN-L)
- 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
- 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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