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. 2016 Aug 11;10:35.
doi: 10.3389/fninf.2016.00035. eCollection 2016.

MINC 2.0: A Flexible Format for Multi-Modal Images

Free PMC article

MINC 2.0: A Flexible Format for Multi-Modal Images

Robert D Vincent et al. Front Neuroinform. .
Free PMC article


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.

Keywords: HDF5; data format; data management; metadata; neuroimaging; provenance.


Figure 1
Figure 1
Implementation of MINC 2.0 in HDF5, illustrating the hierarchical structure. HDF5 groups have names in boldface, attributes are indicated with ellipses. Other rectangles indicate HDF5 datasets.
Figure 2
Figure 2
Voxel vs. world coordinates. Each grid square represents a single sample in the voxel space of the image. The voxel origin (0,0) would be in the upper left corner of the image. The world Y and Z directions are rotated 20⋅ relative to the voxel coordinates. The origin of the world coordinate system would be defined with respect to some anatomical landmark.

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    1. ACR-NEMA committee (1985). Digital Imaging and Communication. Available online at:
    1. ACR-NEMA committee (1988). Digital Imaging and Communication. Available online at:
    1. Ad-Dab'bagh Y., Lyttelton O., Muehlboeck J., Lepage C., Einarson D., Mok K., et al. (2006). The CIVET image-processing environment: a fully automated comprehensive pipeline for anatomical neuroimaging research, in Proceedings of the 12th Annual Meeting of the Organization for Human Brain Mapping (Florence: ).
    1. Amunts K., Lepage C., Borgeat L., Mohlberg H., Dickscheid T., Rousseau M.-É., et al. . (2013). BigBrain: an ultrahigh-resolution 3D human brain model. Science 340, 1472–1475. 10.1126/science.1235381 - DOI - PubMed
    1. Bellec P., Lavoie-Courchesne S., Dickinson P., Lerch J., Zijdenbos A., Evans A. C. (2012). The pipeline system for Octave and Matlab (PSOM): a lightweight scripting framework and execution engine for scientific workflows. Front. Neuroinform. 6:7. 10.3389/fninf.2012.00007 - DOI - PMC - PubMed

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