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Unified segmentation
John Ashburner, Karl John Friston. NeuroImage, 2005-04-02
Topic: Risk, prevention and care in population studies
This study presents a probabilistic framework that combines image registration, tissue classification, and bias correction into a single generative model. The model is based on a mixture of Gaussians extended to incorporate smooth intensity variation and nonlinear registration with tissue probability maps. It provides a derivation of a log-likelihood objective function and describes a strategy, including requisite partial derivatives, for optimising the model parameters.
Key findings
- 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.
Why it matters
This paper describes a single statistical model that combines aligning brain scans, sorting tissue types and correcting image intensity. It is an image-analysis method used to measure the brain in imaging research, not a study of a treatment for Alzheimer's.
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How this study scored 56/100
Published in a peer-reviewed journal. Scored zero for study design, sample size, effect reported, studied in people and randomised / blinded because the record does not state it. We have no data on strength of evidence, so that was left out: it earned 53 of the 95 points we could judge it on, which is 56 out of 100.
| Component | Points | Why |
|---|---|---|
| Journal tier | 7 / 10 | Tier 3 — peer-reviewed journal (NeuroImage) |
| Journal's citation record | 4 / 4 | NeuroImage: journal h-index 523 (OpenAlex, 2026-10-29) |
| Study design | 0 / 12 | No study design recorded — scores zero, not guessed (not stated in the record — scored 0) |
| Strength of evidence | — / 5 | No evidence rating recorded — left out of this score |
| Peer-review validation | 10 / 10 | Peer-reviewed. Published as an article in a named peer-reviewed journal. |
| How recent | 1 / 5 | 2005 — older than 20 years, still counted |
| Sample size | 0 / 4 | Participant count not stated — scores zero (not stated in the record — scored 0) |
| Effect reported | 0 / 3 | No effect size reported — scores zero (not stated in the record — scored 0) |
| Studied in people | 0 / 3 | Not stated whether this was studied in people — scores zero (not stated in the record — scored 0) |
| Randomised / blinded | 0 / 2 | No randomisation or blinding stated — scores zero (not stated in the record — scored 0) |
| Times cited | 7 / 7 | Cited 7,435 times (OpenAlex, 2026-10-10) |
| Citations per year | 5 / 5 | 323.3 citations a year over 23 year(s) since 2005 |
| Impact for its field and year | 6 / 6 | Cited 80.7× the average for papers of its field and year (OpenAlex FWCI) |
| Cited by studies here | 0 / 6 | No other study in this library cites it |
| Still being cited | 4 / 4 | Cited 1,166 times in 2024–2026 (2026 still in progress; OpenAlex) |
| Senior author's track record | 4 / 4 | Last-listed author Karl John Friston: h-index 261 (OpenAlex, whole career) |
| Institution's track record | 2 / 3 | University College London: institution h-index 2,029 (OpenAlex) |
| Multi-centre collaboration | 1 / 3 | OpenAlex lists 3 institution(s) in 1 country |
| Indexed in PubMed | 2 / 2 | Indexed in PubMed (PMID 15955494) |
| Free to read | 0 / 2 | Behind a paywall — scores zero |
| Points | 53 / 95 | Scored on 19 of 20 parts. We have no data for the rest, so they are left out: 53 of the 95 points we could judge it on is 56 out of 100. |
| Total | 56 / 100 | — |
What each of these 20 components measures, and what this score is not →
Read the original
May need a subscription OpenAlex found no free copy of the full text (checked 6 Feb 1020), so the publisher may charge for it. The PubMed abstract is free.
Cite this study APA, Vancouver, BibTeX · download .bib or .ris
Built from the record alzhub holds for this paper, not looked up. Volume, issue and page numbers are not held in the record. Check the citation against the original before you use it.
APA 7th
Ashburner, J., & Friston, K. J. (2005). Unified segmentation. NeuroImage. https://doi.org/10.1016/j.neuroimage.2005.02.018
Vancouver
Ashburner J, Friston KJ. Unified segmentation. NeuroImage. 2005. doi:10.1016/j.neuroimage.2005.02.018. PMID: 15955494.
BibTeX
@article{ashburner2005-15955494,
author = {Ashburner, John and Friston, Karl John},
title = {{Unified segmentation}},
journal = {NeuroImage},
year = {2005},
doi = {10.1016/j.neuroimage.2005.02.018},
pmid = {15955494},
url = {https://doi.org/10.1016/j.neuroimage.2005.02.018}
}