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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Detalles Bibliográficos
Autores principales: Tang, Xiaoying, Crocetti, Deana, Kutten, Kwame, Ceritoglu, Can, Albert, Marilyn S., Mori, Susumu, Mostofsky, Stewart H., Miller, Michael I.
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Frontiers Media S.A. 2015
Materias:
Neuroscience
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC4347448/
https://www.ncbi.nlm.nih.gov/pubmed/25784852
http://dx.doi.org/10.3389/fnins.2015.00061

Internet

https://www.ncbi.nlm.nih.gov/pmc/articles/PMC4347448/
https://www.ncbi.nlm.nih.gov/pubmed/25784852
http://dx.doi.org/10.3389/fnins.2015.00061

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