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The future of MINC. Robert D. Vincent [email protected] Outline. Background New features and functions Compatibility Programming and other nerdy stuff. Background. MINC is built upon a general format netCDF: Network Common Data Form MINC 1.0 is a specialization of this format

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Presentation Transcript

Outline
Outline

  • Background

  • New features and functions

  • Compatibility

  • Programming and other nerdy stuff


Background
Background

  • MINC is built upon a general format

    • netCDF: Network Common Data Form

    • MINC 1.0 is a specialization of this format

  • NetCDF Disadvantages:

    • Not hierarchical

    • Limited data types

    • 32-bit file size

    • Simple, contiguous data storage


Motivation for change
Motivation for change

  • File sizes are increasing

    • Functional imaging, high-resolution anatomical imaging

    • May easily exceed 2 gigabytes

  • Efficient online viewing

    • Storage of multiple pre-computed resolutions

  • Richer data types

    • Vector, tensor, complex voxels

    • Labeled voxels


Hierarchical data format 5
Hierarchical Data Format 5

  • NetCDF Disadvantages

    • Not hierarchical

    • Limited data types

    • 32-bit file size

    • Simple, contiguous data storage model

  • HDF5 Advantages

    • Fully hierarchical

    • Rich set of data types

    • 64-bit file size

    • Complex, powerful data storage model


Minc 2 0 features
MINC 2.0 Features

  • Block-structured storage

  • Labeled volumes

  • Internal compression

  • Multi-resolution images


Block structured storage
Block-structured storage

  • Stores image in a series of N-dimensional blocks

  • May improve performance in some cases

  • Enables internal compression


Block structured storage1
Block-structured storage

  • In an ordinary MINC volume, data is stored in a simple linear arrangement

  • Example: Simple 2x4x4 3D image

16 17 18 19

20 21 22 23

24 25 26 27

28 29 30 31

00 01 02 03 04 05 06 07 08 09 10…

00 01 02 03

04 05 06 07

08 09 10 11

12 13 14 15


Block structured storage2
Block-structured storage

  • In block structured file, nearby voxels are grouped together

  • Example: 2x4x4 image with 2x2x2 blocks

16 17 18 19

20 21 22 23

24 25 26 27

28 29 30 31

00 01 04 05 16 17 20 21 02 03 06…

00 01 02 03

04 05 06 07

08 09 10 11

12 13 14 15


Labeled volumes
Labeled volumes

  • Associates text labels with voxel values

    • Appropriate for classifier output, for example

    • 0 = “Background”, 1 = “White matter”, 2 = “Grey matter”, and so forth

  • Furthers the MINC goal of “self-describing” data files


Internal compression
Internal compression

  • MINC 1.0 External compression

    • External program must decompress an entire file

    • File must be fully decompressed to read a single voxel

    • Uses temporary disk space, bandwidth, and time

  • MINC 2.0 Internal compression

    • Data is compressed on a block-by-block basis

    • Invisible to user, compression is internal to library

    • Nearly random access

    • Parts of the file may be left uncompressed


Multi resolution images
Multi-resolution images

  • Applications

    • Visualization over networks

    • Multi-resolution algorithms

  • Design – Multiple image objects

    • Normal data stored at full resolution

    • Additional images stored at 1/2, 1/4, 1/8, etc.

      • Initially will use a simple voxel averaging algorithm

      • May be enhanced/replaced by us or third parties

    • Adds at most 1/7 of full-resolution size for 3D images


Compatibility
Compatibility

  • MINC programs still support NetCDF

    • Input – Tools automatically “do the right thing”

    • Output – User must explicitly specify “-2” flag for HDF5

  • MINC 1.0 “libminc” still supported

    • Minor enhancements, new flags

    • Some new functions

  • Very few changes to MINC 1.0 code

    • It might actually keep working!


Minc 2 0 programming
MINC 2.0 Programming

  • Philosophy

    • Formalize MINC 1.0 programming idioms

    • Provide a complete set of functions for manipulating image data

    • Backward compatibility

  • Design

    • http://www.bic.mni.mcgill.ca/software/minc2/api_doc-2003-10-31

    • Initial implementation in “C”, with additional language bindings to follow…


Minc 2 0 programming1
MINC 2.0 Programming

  • New concepts

    • volume type – image objects

      • Voxel data

      • Attributes

      • Dimensions

    • dimension type – individual coordinate axes

      • Orientation

      • Spacing


Minc 2 0 programming2
MINC 2.0 Programming

  • MINC 1.0

    • Functions tend to be very general

    • Many of functions serve multiple purposes

    • Programmer must understand both netCDF and MINC concepts

  • MINC 2.0

    • Functions tend to be more specialized

    • Completely hides details of HDF5/netCDF

    • MINC 2.0 must define many more functions (129 and counting vs. less than 50)


Minc 2 0 programming3
MINC 2.0 Programming

  • Example – get one “real” valued voxel

  • MINC 1.0

    icv = miicv_create();

    miicv_setint(icv, MI_ICV_DO_RANGE, TRUE);

    miicv_attach(icv, fd, var_id);

    miicv_get(icv, coords, lengths, &value);

  • MINC 2.0

    miget_real_value(volume,coords,3,&value);


Minc 2 0 programming4
MINC 2.0 Programming

  • Example – direction cosines

  • MINC 1.0

    miattget(fd, varid, Midirection_cosines,

    NC_DOUBLE, 3, cosines, &length);

  • MINC 2.0

    miget_direction_cosines(dimension,cosines);


The future of the future
The future of the future

  • Language support

    • Perl, Python, Java, C++

  • New data classes

    • Geometric objects

    • Transform information

  • Additional compression methods

    • bzip2

    • “Lossy” image compression


Credits
Credits

  • Leila Baghdadi

    • Design, documentation, and programming

  • John Sled

    • Design, documentation, and sanity checking

  • Many others who have offered advice and opinions!

  • netCDF created by UCAR

  • HDF5 created by NCSA

  • Financial support from NIfTI


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