CAT12SANLMDenoising_1st_commit

Author: 0rC0

nipype/interfaces/cat12/__init__.py

@@ -1,4 +1,7 @@
-from .preprocess import CAT12Segment
+from .preprocess import (
+    CAT12Segment,
+    CAT12SANLMDenoising
+)
 from .surface import (
     ExtractAdditionalSurfaceParameters,
     ExtractROIBasedSurfaceMeasures,

nipype/interfaces/cat12/preprocess.py

@@ -593,6 +593,142 @@ def _list_outputs(self):
         return outputs
 
 
+class CAT12SANLMDenoisingInputSpec(SPMCommandInputSpec):
+
+    in_files = InputMultiPath(
+        ImageFileSPM(exists=True),
+        field="data",
+        desc="Images for filtering.",
+        mandatory=True,
+        copyfile=False,
+    )
+
+    spm_type = traits.Enum(
+        16,
+        0,
+        2,
+        512,
+        field='spm_type',
+        usedefault=True,
+        desc='Data type of the output images. 0 = same, 2 = uint8, 512 = uint16, 16 = single (32 bit)'
+
+    )
+
+    intlim = traits.Int(
+        field='intlim',
+        default_value=100,
+        usedefault=True,
+    )
+
+    filename_prefix = traits.Str(
+        field='prefix',
+        default_value='sanlm_',
+        usedefault=True,
+        desc='Filename prefix. Specify  the  string  to be prepended to the filenames of the filtered image file(s). Default prefix is "samlm_".',
+    )
+
+    filename_suffix= traits.Str(
+        field='suffix',
+        default_value='',
+        usedefault=True,
+        desc='Filename suffix. Specify  the  string  to  be  appended  to the filenames of the filtered image file(s). Default suffix is "".'
+    )
+
+    addnoise = traits.Float(default_value=0.5,
+                            usedefault=True,
+                            field='addnoise',
+                            desc='Strength of additional noise in noise-free regions. Add  minimal  amount  of noise in regions without any noise to avoid image segmentation problems. This parameter defines the strength of additional noise as percentage of the average signal intensity.')
+
+    rician = traits.Enum(
+        0,
+        1,
+        field='rician',
+        usedefault=True,
+        desc='''Rician noise
+        MRIs  can  have  Gaussian  or  Rician  distributed  noise with uniform or nonuniform variance across the image. If SNR is high enough
+        (>3)  noise  can  be  well  approximated by Gaussian noise in the foreground. However, for SENSE reconstruction or DTI data a Rician
+        distribution is expected. Please note that the Rician noise estimation is sensitive for large signals in the neighbourhood and can lead to
+        artefacts, e.g. cortex can be affected by very high values in the scalp or in blood vessels.''')
+
+    replaceNANandINF = traits.Enum(
+        1,
+        0,
+        field='replaceNANandINF',
+        usedefault=True,
+        desc='Replace NAN by 0, -INF by the minimum and INF by the maximum of the image.'
+    )
+
+    NCstr = traits.Enum(
+        '-Inf',
+        2,
+        4,
+        field='nlmfilter.optimized.NCstr',
+        usedefault=True,
+        desc='''Strength of Noise Corrections
+        Strength  of  the  (sub-resolution)  spatial  adaptive    non local means (SANLM) noise correction. Please note that the filter strength is
+        automatically  estimated.  Change this parameter only for specific conditions. The "light" option applies half of the filter strength of the
+        adaptive  "medium"  cases,  whereas  the  "strong"  option  uses  the  full  filter  strength,  force sub-resolution filtering and applies an
+        additional  iteration.  Sub-resolution  filtering  is  only  used  in  case  of  high image resolution below 0.8 mm or in case of the "strong"
+        option. light = 2, medium = -Inf, strong = 4'''
+    )
+
+
+class CAT12SANLMDenoisingOutputSpec(TraitedSpec):
+
+    out_file = File(desc='out file')
+
+
+class CAT12SANLMDenoising(SPMCommand):
+    """
+    Spatially adaptive non-local means (SANLM) denoising filter
+
+    This  function  applies  an spatial adaptive (sub-resolution) non-local means denoising filter
+    to  the  data.  This  filter  will  remove  noise  while  preserving  edges. The filter strength is
+    automatically estimated based on the standard deviation of the noise.
+
+    This   filter   is  internally  used  in  the  segmentation  procedure  anyway.  Thus,  it  is  not
+    necessary (and not recommended) to apply the filter before segmentation.
+
+
+    Example:
+    =======
+    from nipype.interfaces import cat12
+    c = cat12.CAT12SANLMDenoising()
+    c.inputs.in_files='sub-test_FLAIR.nii'
+    c.run()
+    """
+
+    input_spec = CAT12SANLMDenoisingInputSpec
+    output_spec = CAT12SANLMDenoisingOutputSpec
+
+    def __init__(self, **inputs):
+        _local_version = SPMCommand().version
+        if _local_version and "12." in _local_version:
+            self._jobtype = "tools"
+            self._jobname = "cat.tools.sanlm"
+
+        SPMCommand.__init__(self, **inputs)
+
+    def _format_arg(self, opt, spec, val):
+        """Convert input to appropriate format for spm"""
+        if opt == "in_files":
+            if isinstance(val, list):
+                return scans_for_fnames(val)
+            else:
+                return scans_for_fname(val)
+
+        return super(CAT12SANLMDenoising, self)._format_arg(opt, spec, val)
+
+    def _list_outputs(self):
+        outputs = self._outputs().get()
+        pth, base, ext = split_filename(self.inputs.in_files[0])
+        outputs['out_file'] = os.path.join(os.getcwd(), self.inputs.filename_prefix +
+                                           base +
+                                           self.inputs.filename_suffix +
+                                           ext)
+        return outputs
+
+
 class Cell2Str(Cell):
     def __str__(self):
         """Convert input to appropriate format for cat12"""