MINC 2.0: A Flexible Format for Multi-Modal Images

• Published in: Frontiers in neuroinformatics Vol. 10; p. 35
• Main Authors: 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
• Format: Journal Article
• Language: English
• Published: Switzerland Frontiers Research Foundation 11.08.2016; Frontiers Media S.A
• Subjects: Alzheimer's disease; Brain research; Compression; data format; data management; HDF5; Initiatives; Medical imaging; Metadata; Neuroimaging; Neuroscience; provenance; Studies; Canada; Montreal Quebec Canada; data format; metadata; HDF5; data management; provenance; neuroimaging
• ISSN: 1662-5196; 1662-5196
• DOI: 10.3389/fninf.2016.00035

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.