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...
| Main Authors: | , , , , , , , , , , , , , |
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| Format: | Article |
| Language: | English |
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Frontiers Media S.A.
2016-08-01
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| Series: | Frontiers in Neuroinformatics |
| Subjects: | |
| Online Access: | http://journal.frontiersin.org/Journal/10.3389/fninf.2016.00035/full |
| _version_ | 1811323894692315136 |
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| author | Robert D. Vincent Peter Neelin Najmeh Khalili-Mahani Andrew Lindsay Janke Vladimir S. Fonov Steven M. Robbins Leila Baghdadi Jason Lerch Jason Lerch John G. Sled John G. Sled Reza Adalat David MacDonald Alex P. Zijdenbos D. Louis Collins D. Louis Collins Alan Charles Evans |
| author_facet | Robert D. Vincent Peter Neelin Najmeh Khalili-Mahani Andrew Lindsay Janke Vladimir S. Fonov Steven M. Robbins Leila Baghdadi Jason Lerch Jason Lerch John G. Sled John G. Sled Reza Adalat David MacDonald Alex P. Zijdenbos D. Louis Collins D. Louis Collins Alan Charles Evans |
| author_sort | Robert D. Vincent |
| collection | DOAJ |
| 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. |
| first_indexed | 2024-04-13T14:03:58Z |
| format | Article |
| id | doaj.art-c2ebce1001564a03bbceade6909f4210 |
| institution | Directory Open Access Journal |
| issn | 1662-5196 |
| language | English |
| last_indexed | 2024-04-13T14:03:58Z |
| publishDate | 2016-08-01 |
| publisher | Frontiers Media S.A. |
| record_format | Article |
| series | Frontiers in Neuroinformatics |
| spelling | doaj.art-c2ebce1001564a03bbceade6909f42102022-12-22T02:43:58ZengFrontiers Media S.A.Frontiers in Neuroinformatics1662-51962016-08-011010.3389/fninf.2016.00035206796MINC 2.0: a flexible format for multi-modal imagesRobert D. Vincent0Peter Neelin1Najmeh Khalili-Mahani2Andrew Lindsay Janke3Vladimir S. Fonov4Steven M. Robbins5Leila Baghdadi6Jason Lerch7Jason Lerch8John G. Sled9John G. Sled10Reza Adalat11David MacDonald12Alex P. Zijdenbos13D. Louis Collins14D. Louis Collins15Alan Charles Evans16McGill UniversityIntelerad Medical SystemsMcGill UniversityThe University of QueenslandMcGill UniversityMcGill UniversityThe Hospital for Sick ChildrenThe Hospital for Sick ChildrenUniversity of TorontoThe Hospital for Sick ChildrenUniversity of TorontoMcGill UniversityAutodesk, Inc.Biospective, Inc.McGill UniversityMcGill UniversityMcGill UniversityIt 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.http://journal.frontiersin.org/Journal/10.3389/fninf.2016.00035/fullNeuroimagingdata managementmetadataprovenanceHDF5data format |
| spellingShingle | Robert D. Vincent Peter Neelin Najmeh Khalili-Mahani Andrew Lindsay Janke Vladimir S. Fonov Steven M. Robbins Leila Baghdadi Jason Lerch Jason Lerch John G. Sled John G. Sled Reza Adalat David MacDonald Alex P. Zijdenbos D. Louis Collins D. Louis Collins Alan Charles Evans MINC 2.0: a flexible format for multi-modal images Frontiers in Neuroinformatics Neuroimaging data management metadata provenance HDF5 data format |
| 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 | Neuroimaging data management metadata provenance HDF5 data format |
| url | http://journal.frontiersin.org/Journal/10.3389/fninf.2016.00035/full |
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