intensity-normalization

This package contains various routines to normalize the intensity of various contrasts of magnetic resonance (MR) brain images; specifically, T1-weighted (T1-w), T2-weighted (T2-w), and FLuid-Attenuated Inversion Recovery (FLAIR). Intensity normalization is an important pre-processing step in many image processing applications regarding MR images since MR images do not have a consistent intensity scale. We implement various individual image-based and sample-based (i.e., a set of images of the same contrast taken from the same scanner) intensity normalization routines to help alleviate this issue.

We implement the following normalization methods:

• Z-score normalization
• Fuzzy C-means (FCM)-segmentation-based white matter (WM) mean normalization
• Gaussian Mixture Model (GMM)-based WM mean normalization
• Kernel Density Estimate WM Peak normalization
• Piecewise Linear Histogram Matching [1,2]
• WhiteStripe [3]
• RAVEL [4]

We use this package to explore the impact of intensity normalization on a synthesis task (pre-print available here).

** Note that while this release was carefully inspected, there may be bugs. Please submit an issue if you encounter a problem. **

This package was developed by Jacob Reinhold and the other students and researchers of the Image Analysis and Communication Lab (IACL).

Link to main Gitlab Repository

Requirements

• matplotlib
• numpy
• nibabel
• scikit-fuzzy
• scikit-learn
• scipy
• statsmodels

We have provided a script create_env.sh to create a conda environment with the necessary packages (run like: . ./create_env.sh, this package will be installed in the created environment)

Basic Usage

The easiest way to install the package is through the following command:

pip install git+git://github.com/jcreinhold/intensity-normalization.git

To install from the source directory, use

python setup.py install

or (if you actively want to make changes to the package)

python setup.py develop

and use the several provided command line scripts to interface with the package, e.g.,

fcm-normalize -i t1/ -m masks/ -o test_fcm -v

where t1/ is a directory full of N T1-w images and masks/ is a directory full of N corresponding brain masks, test_fcm is the output directory for the normalized images, and -v controls the verbosity of the output.

Note the package antspy is required for the RAVEL normalization routine, the preprocessing tool as well as the co-registration tool, but all other normalization and processing tools work without it. To also install the antspy package either append --antspy to your call to setup.py or create_env.sh. The installation of antspy may not work, in which case you can either (if you are on linux) append --1.4 to your list of input arguments to setup.py (which installs a previous version of the binaries of antspy or you can just build antspy from source.

The command line interface is standard across all normalization routines (i.e., you should be able to run all normalization routines with the same call as in the above example), however each has unique options. Call any executable script with the -h flag to see more detailed instructions about the proper call.

Tutorial

5 minute Overview

In addition to the above small tutorial, there is consolidated documentation here.

Test Package

Unit tests can be run from the main directory as follows:

nosetests -v --with-coverage --cover-tests --cover-package=intensity_normalization tests

If you are using docker, then the equivalent command will be (depending on how the image was built):

docker run jcreinhold/intensity-normalization /bin/bash -c "pip install nose && nosetests -v tests/"

Singularity

You can build a singularity image from the docker image hosted on dockerhub via the following command:

singularity pull --name intensity_normalization.simg docker://jcreinhold/intensity-normalization

Citation

If you use the intensity-normalization package in an academic paper, please cite the corresponding paper:

@inproceedings{reinhold2019evaluating,
  title={Evaluating the impact of intensity normalization on {MR} image synthesis},
  author={Reinhold, Jacob C and Dewey, Blake E and Carass, Aaron and Prince, Jerry L},
  booktitle={Medical Imaging 2019: Image Processing},
  volume={10949},
  pages={109493H},
  year={2019},
  organization={International Society for Optics and Photonics}
}

References

[1] N. Laszlo G and J. K. Udupa, “On Standardizing the MR Image Intensity Scale,” Magn. Reson. Med., vol. 42, pp. 1072–1081, 1999.

[2] M. Shah, Y. Xiao, N. Subbanna, S. Francis, D. L. Arnold, D. L. Collins, and T. Arbel, “Evaluating intensity normalization on MRIs of human brain with multiple sclerosis,” Med. Image Anal., vol. 15, no. 2, pp. 267–282, 2011.

[3] R. T. Shinohara, E. M. Sweeney, J. Goldsmith, N. Shiee, F. J. Mateen, P. A. Calabresi, S. Jarso, D. L. Pham, D. S. Reich, and C. M. Crainiceanu, “Statistical normalization techniques for magnetic resonance imaging,” NeuroImage Clin., vol. 6, pp. 9–19, 2014.

[4] J. P. Fortin, E. M. Sweeney, J. Muschelli, C. M. Crainiceanu, and R. T. Shinohara, “Removing inter-subject technical variability in magnetic resonance imaging studies,” NeuroImage, vol. 132, pp. 198–212, 2016.