Segmentation of brain magnetic resonance images based on multi-atlas likelihood fusion: testing using data with a broad range of anatomical and photometric profiles

We propose a hierarchical pipeline for skull-stripping and segmentation of anatomical structures of interest from T1-weighted images of the human brain. The pipeline is constructed based on a two-level Bayesian parameter estimation algorithm called multi-atlas likelihood fusion (MALF). In MALF, esti...

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Dettagli Bibliografici
Pubblicato in:Front Neurosci
Autori principali: Tang, Xiaoying, Crocetti, Deana, Kutten, Kwame, Ceritoglu, Can, Albert, Marilyn S., Mori, Susumu, Mostofsky, Stewart H., Miller, Michael I.
Natura: Artigo
Lingua:Inglês
Pubblicazione: Frontiers Media S.A. 2015
Soggetti:
Neuroscience
Accesso online:https://ncbi.nlm.nih.gov/pmc/articles/PMC4347448/
https://ncbi.nlm.nih.gov/pubmed/25784852
https://ncbi.nlm.nih.govhttp://dx.doi.org/10.3389/fnins.2015.00061
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