chase-woodward
Uploaded by
18 SLIDES
297 VIEWS
180LIKES

Exploring MINC Toolset: Visualization, Processing, and Command-line Tools for Neuroimaging

DESCRIPTION

This presentation provides a comprehensive overview of the MINC (Medical Image NetCDF) toolset, designed for neuroimaging data processing and visualization. It covers essential topics such as the structure of MINC files, displaying images, resampling, reshaping datasets, and performing mathematical operations. Attendees will learn to navigate command-line utilities like `mincinfo`, `mincstats`, and `mincresample`, and understand image data as n-dimensional arrays. This session is ideal for researchers and clinicians involved in neuroimaging who are seeking to enhance their data processing skills with MINC.

1 / 18

Download Presentation

Exploring MINC Toolset: Visualization, Processing, and Command-line Tools for Neuroimaging

An Image/Link below is provided (as is) to download presentation Download Policy: Content on the Website is provided to you AS IS for your information and personal use and may not be sold / licensed / shared on other websites without getting consent from its author. Content is provided to you AS IS for your information and personal use only. Download presentation by click this link. While downloading, if for some reason you are not able to download a presentation, the publisher may have deleted the file from their server. During download, if you can't get a presentation, the file might be deleted by the publisher.

E N D

Presentation Transcript

  1. Steve Robbins <stever@bic.mni.mcgill.ca> MINC meeting 2003A Short Tour of the MINC Toolset

  2. MINC-aware Programs Visualization: - Display - register - jiv Command-line Tools

  3. Outline Inside a MINC file Example tools information resamping reshaping arbitrary math Getting more info

  4. Inside a MINC File Image data n-dimensional array real-valued sometimes interpreted as “labels” e.g. 0=background, 1=CSF, 2=Gray, 3=White Shape of image array order of array axes length of array along each axis Location of image array in world space

  5. mincinfo mincinfo mri_001.mnc input: file mri_001.mnc output (text on terminal): > mincinfo mri_001.mnc file: mri_001.mnc image: signed__ short 0 to 4095 image dimensions: zspace yspace xspace dimension name length step start -------------- ------ ---- ----- zspace 181 1 -72 yspace 217 1 -126 xspace 181 1 -90

  6. mincstats mincstats mri_001.mnc input: file mri_001.mnc output: text on terminal, histogram file > mincstats mri_001.mnc ... Volume (mm3): 7109137 Min: 0 Max: 1115712.374 Sum: 1.221982704e+12 Sum^2: 4.347383143e+17 Mean: 171889.0357 Variance: 3.160621567e+10 Stddev: 177781.3704 ...

  7. mincresample: Change Sampling Grid mincresample -like template.mnc mri_001.mnc res_001.mnc input: mri_001.mnc, template.mnc output: res_001.mnc mri_001.mnc template.mnc

  8. mincresample: interpolation trilinear default, fast tricubic smoother, slower nearest_neighbour useful for label volumes

  9. mincresample: Spatial Transformation T describes transformation from scanner (native) space to standard space standard space could be Talairach, MNI_305, ... T native standard

  10. mincresample: Spatial Transformation mincresample -transformation T.xfm -like template.mnc mri_001.mnc res_001.mnc input: mri_001.mnc, T.xfm (T_grid_0.mnc), template.mnc output: res_001.mnc

  11. mincreshape: change image array extract a sub-block (“hyperslab”) extract 2D slice of 3D file extract 3D time point of 4D (x,y,z,t) file enlarge by zero-padding useful with label file created using “Display”

  12. mincreshape (2) re-order dimensions e.g. transverse to coronal no interpolation: new grid coincident with old unlike mincresample

  13. mincaverage (1) mincaverage mri_*.mnc average.mnc input: mri_*.mnc files (all of same shape!) output: average.mnc optionally normalize intensity before averaging

  14. mincaverage (2) mincaverage -binarize -binvalue 2 seg_*.mnc gray_prob.mnc each input is “binarized” result at each voxel: probability of gray matter

  15. mincmath: Simple Math mincmath -sub mri_001.mnc mri_002.mnc diff.mnc input: mri_001.mnc, mri_002.mnc output: diff.mnc

  16. minccalc: Complex Math minccalc -expr 'A[0] == 3 && A[1] != 3' seg_001.mnc seg_002.mnc discrep.mnc input: seg_001.mnc, seg_002.mnc output: discrep.mnc rich set of operations expression may contain multiple statements any number of input files multiple output files

  17. Common Options clobber overwrite existing file quiet, verbose, debug default is -verbose image storage byte, short, int, etc

  18. Getting Help mincmath -help terse reminder of options man mincmath manual page minc-users @ bic.mni.mcgill.ca friendly, helpful humans

More Related