How to normalize intensity of brain images to the range [0, 1] with nipype and FSL.

nipype_normalize.py

#!/usr/bin/env python

"""
How to normalize intensity of brain images to the range [0, 1] with nipype and FSL.
"""

import os.path

import nipype
import nipype.interfaces
import nipype.interfaces.fsl as fsl

# Assume that inputs and outputs live in subdirectories of this directory:
base_dir = os.path.join(os.environ['HOME'], 'project')

# Source and sink:
grabber = nipype.Node(interface=nipype.DataGrabber(infields=['arg'],
                                                   outfields=['out_file']),
                      name='grabber')                      
grabber.inputs.base_directory = os.path.join(base_dir, 'input')
grabber.inputs.sort_filelist = False
grabber.inputs.template = '%s.nii'
grabber.inputs.arg = '*'

# Use substition to force all output files to be dumped into the same
# directory:
sink = nipype.Node(interface=nipype.DataSink(),
                   name='sink')
sink.inputs.base_directory = os.path.join(base_dir, 'output')
sink.inputs.regexp_substitutions = [('_\w+\d+', '.')]

# Intensity normalization - subtract minimum, then divide by difference of maximum and minimum:
fslstats = nipype.MapNode(interface=fsl.ImageStats(op_string='-R'),
                          name='fslstats', iterfield=['in_file'])
def func(in_stat):
    min_val, max_val = in_stat
    return '-sub %s -div %s' % (min_val, (max_val-min_val))
stat_to_op_string = nipype.MapNode(interface=nipype.interfaces.Function(input_names=['in_stat'],
                                                                        output_names=['op_string'],
                                                                        function=func),
                                   name='stat_to_op_string', iterfield=['in_stat'])
fslmaths = nipype.MapNode(interface=fsl.ImageMaths(),
                          name='fslmaths', iterfield=['in_file', 'op_string'])

# Use @ to prevent creation of subdirectories in sink's base directory
# when saving output:
workflow = nipype.Workflow('workflow')
workflow.connect([(grabber, fslstats, [('out_file', 'in_file')]),
                  (grabber, fslmaths, [('out_file', 'in_file')]),
                  (fslstats, stat_to_op_string, [('out_stat', 'in_stat')]),
                  (stat_to_op_string, fslmaths, [('op_string', 'op_string')]),
                  (fslmaths, sink, [('out_file', '@in_file')])])

workflow.run('MultiProc', plugin_args={'n_procs': 4})