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MINC/Authors

< MINC
The latest reviewed version was checked on 16 August 2017. There is 1 pending change awaiting review.

Authors of MINC

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Many People have contributed to MINC over the years. Peter Neelin originally conceived and wrote MINC in about 1992, here is a list of others who have contributed parts of MINC proper (in alphabetical order).

  • Leila Baghdadi - MINC2 coding
  • Louis Collins - mni_autoreg, classify and others
  • Mishkin Derakhshan - many bugfixes
  • Vladimir Fonov - many bugfixes
  • Andrew Janke - Maintenance, binary packaging, documentation
  • Jason Lerch - brain view and other surface things
  • Dave McDonald - conglomerate, register, Display
  • Claude LePage - many bugfixes to many packages
  • Steve Robbins - surface code, Debian packaging
  • John Sled - N3
  • Jussi Tohka - PVE
  • Bert Vincent - coded MINC2
  • Mark Wolforth (wolforth pet.mni.mcgill.ca) - contributed to pages linked at MINC/Tools/emma
  • Greg Ward (greg bic.mni.mcgill.ca) - contributed to pages linked at MINC/Tools/emma
  • Alex Zijdenbos - Classify

How to reference MINC

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If you need to reference MINC itself in a paper, the best option is to use one of these two web links 

  http://www.bic.mni.mcgill.ca/ServicesSoftware
  http://en.wikibooks.org/wiki/MINC

Some standard text you might include:

Medical Imaging NetCDF (MINC) is a medical imaging data format and associated set of tools and libraries developed at the Montreal Neurological Institute (MNI) and freely available online


References for particular tools are given below

classify

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classify is a segmentation package that includes the classify_clean script.

The tool classify_clean, which is used to classify stereotaxic MINC volumes, involves a Bayesian labeling scheme and a set of standard sample points to compute an initial volume classification. This classification is then employed to purge incorrect tag points from the standard set, yielding a custom set of labels for the particular subject. Finally, this tag point set is used by an artificial neural net classifier to classify the volume (Zijdenbos et al., 1998).

Zijdenbos, A., Forghani, R., Evans, A., 1998. Automatic quantificationof MS lesions in 3D MRI brain data sets: validation of INSECT. Medical Image Computing and Computer-Assisted Interventation—MICCAI'98, pp. 439–448

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