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MINC 2.0: A Flexible Format for Multi-Modal Images

It is often useful that an imaging data format can afford rich metadata, be flexible, scale to very large file sizes, support multi-modal data, and have strong inbuilt mechanisms for data provenance. Beginning in 1992, MINC was developed as a system for flexible, self-documenting representation of n...

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Autores principales: Vincent, Robert D., Neelin, Peter, Khalili-Mahani, Najmeh, Janke, Andrew L., Fonov, Vladimir S., Robbins, Steven M., Baghdadi, Leila, Lerch, Jason, Sled, John G., Adalat, Reza, MacDonald, David, Zijdenbos, Alex P., Collins, D. Louis, Evans, Alan C.
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Frontiers Media S.A. 2016
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC4980430/
https://www.ncbi.nlm.nih.gov/pubmed/27563289
http://dx.doi.org/10.3389/fninf.2016.00035
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author Vincent, Robert D.
Neelin, Peter
Khalili-Mahani, Najmeh
Janke, Andrew L.
Fonov, Vladimir S.
Robbins, Steven M.
Baghdadi, Leila
Lerch, Jason
Sled, John G.
Adalat, Reza
MacDonald, David
Zijdenbos, Alex P.
Collins, D. Louis
Evans, Alan C.
author_facet Vincent, Robert D.
Neelin, Peter
Khalili-Mahani, Najmeh
Janke, Andrew L.
Fonov, Vladimir S.
Robbins, Steven M.
Baghdadi, Leila
Lerch, Jason
Sled, John G.
Adalat, Reza
MacDonald, David
Zijdenbos, Alex P.
Collins, D. Louis
Evans, Alan C.
author_sort Vincent, Robert D.
collection PubMed
description It is often useful that an imaging data format can afford rich metadata, be flexible, scale to very large file sizes, support multi-modal data, and have strong inbuilt mechanisms for data provenance. Beginning in 1992, MINC was developed as a system for flexible, self-documenting representation of neuroscientific imaging data with arbitrary orientation and dimensionality. The MINC system incorporates three broad components: a file format specification, a programming library, and a growing set of tools. In the early 2000's the MINC developers created MINC 2.0, which added support for 64-bit file sizes, internal compression, and a number of other modern features. Because of its extensible design, it has been easy to incorporate details of provenance in the header metadata, including an explicit processing history, unique identifiers, and vendor-specific scanner settings. This makes MINC ideal for use in large scale imaging studies and databases. It also makes it easy to adapt to new scanning sequences and modalities.
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spelling pubmed-49804302016-08-25 MINC 2.0: A Flexible Format for Multi-Modal Images Vincent, Robert D. Neelin, Peter Khalili-Mahani, Najmeh Janke, Andrew L. Fonov, Vladimir S. Robbins, Steven M. Baghdadi, Leila Lerch, Jason Sled, John G. Adalat, Reza MacDonald, David Zijdenbos, Alex P. Collins, D. Louis Evans, Alan C. Front Neuroinform Neuroscience It is often useful that an imaging data format can afford rich metadata, be flexible, scale to very large file sizes, support multi-modal data, and have strong inbuilt mechanisms for data provenance. Beginning in 1992, MINC was developed as a system for flexible, self-documenting representation of neuroscientific imaging data with arbitrary orientation and dimensionality. The MINC system incorporates three broad components: a file format specification, a programming library, and a growing set of tools. In the early 2000's the MINC developers created MINC 2.0, which added support for 64-bit file sizes, internal compression, and a number of other modern features. Because of its extensible design, it has been easy to incorporate details of provenance in the header metadata, including an explicit processing history, unique identifiers, and vendor-specific scanner settings. This makes MINC ideal for use in large scale imaging studies and databases. It also makes it easy to adapt to new scanning sequences and modalities. Frontiers Media S.A. 2016-08-11 /pmc/articles/PMC4980430/ /pubmed/27563289 http://dx.doi.org/10.3389/fninf.2016.00035 Text en Copyright © 2016 Vincent, Neelin, Khalili-Mahani, Janke, Fonov, Robbins, Baghdadi, Lerch, Sled, Adalat, MacDonald, Zijdenbos, Collins and Evans. http://creativecommons.org/licenses/by/4.0/ This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY). The use, distribution or reproduction in other forums is permitted, provided the original author(s) or licensor are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms.
spellingShingle Neuroscience
Vincent, Robert D.
Neelin, Peter
Khalili-Mahani, Najmeh
Janke, Andrew L.
Fonov, Vladimir S.
Robbins, Steven M.
Baghdadi, Leila
Lerch, Jason
Sled, John G.
Adalat, Reza
MacDonald, David
Zijdenbos, Alex P.
Collins, D. Louis
Evans, Alan C.
MINC 2.0: A Flexible Format for Multi-Modal Images
title MINC 2.0: A Flexible Format for Multi-Modal Images
title_full MINC 2.0: A Flexible Format for Multi-Modal Images
title_fullStr MINC 2.0: A Flexible Format for Multi-Modal Images
title_full_unstemmed MINC 2.0: A Flexible Format for Multi-Modal Images
title_short MINC 2.0: A Flexible Format for Multi-Modal Images
title_sort minc 2.0: a flexible format for multi-modal images
topic Neuroscience
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC4980430/
https://www.ncbi.nlm.nih.gov/pubmed/27563289
http://dx.doi.org/10.3389/fninf.2016.00035
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