ENH: Wrapper for DenoiseImage

[jessicaforbes]

This provides a Nipype wrapper for the DenoiseImage program provided as part of ANTs.

It is a fairly simple interface seen here:

$ ./DenoiseImage
COMMAND:
DenoiseImage
Denoise an image using a spatially adaptive filter originally described in J. V.
Manjon, P. Coupe, Luis Marti-Bonmati, D. L. Collins, and M. Robles. Adaptive
Non-Local Means Denoising of MR Images With Spatially Varying Noise Levels,
Journal of Magnetic Resonance Imaging, 31:192-203, June 2010.
OPTIONS:
-d, --image-dimensionality 2/3/4
This option forces the image to be treated as a specified-dimensional image. If
not specified, the program tries to infer the dimensionality from the input
image.
-i, --input-image inputImageFilename
A scalar image is expected as input for noise correction.
-n, --noise-model Rician/(Gaussian)
Employ a Rician or Gaussian noise model.
-s, --shrink-factor (1)/2/3/...
Running noise correction on large images can be time consuming. To lessen
computation time, the input image can be resampled. The shrink factor, specified
as a single integer, describes this resampling. Shrink factor = 1 is the
default.
-o, --output correctedImage
[correctedImage,]
The output consists of the noise corrected version of the input image.
Optionally, one can also output the estimated noise image.
--version
Get Version Information.
-v, --verbose (0)/1
Verbose output.
-h
Print the help menu (short version).
--help
Print the help menu.

[blakedewey]

This looks like a good addition, but it needs a few changes before we can merge it.

1. We don't have the need for version, help or short_help options, as nipype is a pipelining tool and a pipeline would not need to see the output if those options are set.
2. In the ANTs interfaces, we have developed a good way to specify more than one output file. See if you can replicate the style used in N4BiasFieldCorrection.

If you have any questions, just let me know.

[jessicaforbes]

@blakedewey I implemented your suggestions of removing the help/version variables and replicating the output style used in N4BiasFieldCorrection. The tests are currently running.

Thank you for the tips.

[blakedewey]

@satra What is with the codacy failure?

[chrisgorgo]

you can skip genfile (and the need to define _gen_filename) if you use name_source and name_template (more info here: http://nipy.org/nipype/devel/cmd_interface_devel.html#creating-outputs-on-the-fly). This will simplify the interface.

[chrisgorgo]

same thing applies for noise_image

[jessicaforbes]

For output_image and noise_image I am setting keep_extension=True, but this does not seem to work. The output_image does not have an extension. Can someone please explain if there is an error with keep_extension or if I need to change something?

output_image = File(argstr="-o %s", name_source=['input_image'], hash_files=False,
keep_extension=True, name_template='%s_noise_corrected',
desc='The output consists of the noise corrected '
'version of the input image.')

Error with the Travis CI build is due to missing extensions in the cmdline outputs:

File "/home/travis/build/nipy/nipype/nipype/interfaces/ants/segmentation.py", line 843, in nipype.interfaces.ants.segmentation.DenoiseImage
Failed example:
denoise.cmdline
Expected:
'DenoiseImage -d 3 -i im1.nii -n Gaussian -o im1_noise_corrected.nii -s 1'
Got:
'DenoiseImage -d 3 -i im1.nii -n Gaussian -o im1_noise_corrected -s 1'

File "/home/travis/build/nipy/nipype/nipype/interfaces/ants/segmentation.py", line 856, in nipype.interfaces.ants.segmentation.DenoiseImage
Failed example:
denoise_3.cmdline
Expected:
'DenoiseImage -i im1.nii -n Gaussian -o [ im1_noise_corrected.nii, im1_noise.nii ] -s 1'
Got:
'DenoiseImage -i im1.nii -n Gaussian -o [ im1_noise_corrected, im1_noise ] -s 1'

[jessicaforbes]

Thank you @satra for fixing the keep_extension metadata behavior.

[jessicaforbes]

I am not sure what the codacy issue is or how to fix it.

[chrisgorgo]

Codacy claims that this PR increased "complexity" of the code, but does not provide much details. Let's ignore this one. Thank you for your contribution!