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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...
Autores principales: | , , , , , , , , , , , , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
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Frontiers Media S.A.
2016
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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. |
format | Online Article Text |
id | pubmed-4980430 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2016 |
publisher | Frontiers Media S.A. |
record_format | MEDLINE/PubMed |
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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