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<main>





  
  

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<article id="content">





  
  

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<header>





  
  

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<h1 class="title">Module <code>pymskt.image.main</code></h1>





  
  

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</header>





  
  

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<section id="section-intro">





  
  

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<details class="source">





  
  

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<summary>





  
  

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<span>Expand source code</span>





  
  

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</summary>





  
  

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<pre><code class="python">from typing import Optional





  
  

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import os





  
  

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import vtk





  
  

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import SimpleITK as sitk





  
  

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import numpy as np





  
  

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from vtk.util.numpy_support import numpy_to_vtk





  
  

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def set_vtk_image_origin(vtk_image, new_origin=(0, 0, 0)):





  
  

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    &#34;&#34;&#34;





  
  

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    Reset the origin of a `vtk_image`





  
  

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    Parameters





  
  

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    ----------





  
  

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    vtk_image : vtk.image





  
  

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        VTK image that we want to change the origin of. 





  
  

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    new_origin : tuple, optional





  
  

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        New origin to asign to `vtk_image`, by default (0, 0, 0)





  
  

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    Returns





  
  

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    -------





  
  

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    vtk.Filter





  
  

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        End of VTK filter pipeline after applying origin change. 





  
  

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    &#34;&#34;&#34;    





  
  

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    change_origin = vtk.vtkImageChangeInformation()





  
  

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    change_origin.SetInputConnection(vtk_image.GetOutputPort())





  
  

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    change_origin.SetOutputOrigin(new_origin)





  
  

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    change_origin.Update()





  
  

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    return change_origin





  
  

  59
  





  
  

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def read_nrrd(path, set_origin_zero=False):





  
  

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    &#34;&#34;&#34;





  
  

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    Read NRRD image file into vtk. Enables usage of marching cubes





  
  

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    and other functions that work on image data.





  
  

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    Parameters





  
  

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    ----------





  
  

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    path : str





  
  

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        Path to `.nrrd` medical image to read in. 





  
  

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    set_origin_zero : bool, optional





  
  

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        Bool to determine if origin should be set to zeros, by default False





  
  

  71
  





  
  

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    Returns





  
  

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    -------





  
  

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    vtk.Filter





  
  

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        End of VTK filter pipeline. 





  
  

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    &#34;&#34;&#34;    





  
  

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    image_reader = vtk.vtkNrrdReader()





  
  

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    image_reader.SetFileName(path)





  
  

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    image_reader.Update()





  
  

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    if set_origin_zero is True:





  
  

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        change_origin = set_vtk_image_origin(image_reader, new_origin=(0, 0, 0))





  
  

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        return change_origin





  
  

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    elif set_origin_zero is False:





  
  

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        return image_reader





  
  

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def set_seg_border_to_zeros(seg_image,





  
  

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                            border_size=1):





  
  

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    &#34;&#34;&#34;





  
  

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    Utility function to ensure that all segmentations are &#34;closed&#34; after marching cubes. 





  
  

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    If the segmentation extends to the edges of the image then the surface wont be closed 





  
  

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    at the places it touches the edges. 





  
  

  94
  





  
  

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    Parameters





  
  

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    ----------





  
  

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    seg_image : SimpleITK.Image





  
  

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        Image of a segmentation. 





  
  

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    border_size : int, optional





  
  

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        The size of the border to set around the edges of the 3D image, by default 1





  
  

  101
  





  
  

  102
  
    Returns





  
  

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    -------





  
  

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    SimpleITK.Image





  
  

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        The image with border set to 0 (background). 





  
  

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    &#34;&#34;&#34;    





  
  

  107
  





  
  

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    seg_array = sitk.GetArrayFromImage(seg_image)





  
  

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    new_seg_array = np.zeros_like(seg_array)





  
  

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    new_seg_array[border_size:-border_size, border_size:-border_size, border_size:-border_size] = seg_array[border_size:-border_size, border_size:-border_size, border_size:-border_size]





  
  

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    new_seg_image = sitk.GetImageFromArray(new_seg_array)





  
  

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    new_seg_image.CopyInformation(seg_image)





  
  

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    return new_seg_image





  
  

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def smooth_image(image, label_idx, variance=1.0):





  
  

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    &#34;&#34;&#34;





  
  

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    Smooth a single label in a SimpleITK image. Used as pre-processing for 





  
  

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    bones/cartilage before applying marching cubes. Helps obtain smooth surfaces. 





  
  

  120
  





  
  

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    Parameters





  
  

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    ----------





  
  

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    image : SimpleITK.Image





  
  

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        Image to be smoothed. 





  
  

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    label_idx : int





  
  

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        Integer of the tissue of interest to be smoothed in the image. 





  
  

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    variance : float, optional





  
  

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        The size of the smoothing, by default 1.0





  
  

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    Returns





  
  

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    -------





  
  

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    SimpleITK.Image





  
  

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        Image of only the label (tissue) of interest after being smoothed. 





  
  

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    &#34;&#34;&#34;    





  
  

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    new_image = binarize_segmentation_image(image, label_idx)





  
  

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    new_image = sitk.Cast(new_image, sitk.sitkFloat32)





  
  

  138
  





  
  

  139
  
    gauss_filter = sitk.DiscreteGaussianImageFilter()





  
  

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    gauss_filter.SetVariance(variance)





  
  

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    #     gauss_filter.SetUseImageSpacingOn





  
  

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    gauss_filter.SetUseImageSpacing(True)





  
  

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    filtered_new_image = gauss_filter.Execute(new_image)





  
  

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    return filtered_new_image





  
  

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def binarize_segmentation_image(seg_image, label_idx):





  
  

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    &#34;&#34;&#34;





  
  

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    Return segmentation that is only 0s/1s, with 1s where label_idx is





  
  

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    located in the image. 





  
  

  151
  





  
  

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    Parameters





  
  

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    ----------





  
  

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    seg_image : SimpleITK.Image





  
  

  155
  
        Segmentation image that contains data we want to binarize





  
  

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    label_idx : int





  
  

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        Integer/label that we want to extract (binarize) from the





  
  

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        `seg_image`.





  
  

  159
  





  
  

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    Returns





  
  

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    -------





  
  

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    SimpleITK.Image





  
  

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        New segmentation image that is binarized. 





  
  

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    &#34;&#34;&#34;    





  
  

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    array = sitk.GetArrayFromImage(seg_image)





  
  

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    array_ = np.zeros_like(array)





  
  

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    array_[array == label_idx] = 1





  
  

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    new_seg_image = sitk.GetImageFromArray(array_)





  
  

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    new_seg_image.CopyInformation(seg_image)





  
  

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    return new_seg_image





  
  

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def crop_bone_based_on_width(seg_image,





  
  

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                             bone_idx,





  
  

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                             np_med_lat_axis=0,





  
  

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                             np_inf_sup_axis=1,





  
  

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                             bone_crop_distal=True,





  
  

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                             value_to_reassign=0,





  
  

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                             percent_width_to_crop_height=1.0):





  
  

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    &#34;&#34;&#34;





  
  

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    Crop the bone labelmap of a SimpleITK.Image so that it is proportional to the





  
  

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    bones medial/lateral width. 





  
  

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    Parameters





  
  

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    ----------





  
  

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    seg_image : SimpleITK.Image





  
  

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        Image to be cropped. 





  
  

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    bone_idx : int





  
  

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        Label_index of the bone to be cropped. 





  
  

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    np_med_lat_axis : int, optional





  
  

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        Medial/lateral axis, by default 0





  
  

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    np_inf_sup_axis : int, optional





  
  

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        Inferior/superir axis, by default 1





  
  

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    bone_crop_distal : bool, optional





  
  

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        Boolean of cropping should occur distal or proximally, by default True





  
  

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    value_to_reassign : int, optional





  
  

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        Value to replace bone label with, by default 0





  
  

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    percent_width_to_crop_height : float, optional





  
  

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        Bone length as a proportion of width, by default 1.0





  
  

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    Returns





  
  

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    -------





  
  

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    SimpleITK.Image





  
  

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        Image after bone is cropped as a proportion of the bone&#39;s width. 





  
  

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    &#34;&#34;&#34;    





  
  

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    seg_array = sitk.GetArrayFromImage(seg_image)





  
  

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    loc_bone = np.where(seg_array == bone_idx)





  
  

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    med_lat_width_bone_mm = (np.max(loc_bone[np_med_lat_axis]) - np.min(loc_bone[np_med_lat_axis])) * \





  
  

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                            seg_image.GetSpacing()[::-1][np_med_lat_axis]





  
  

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    inf_sup_crop_in_pixels = (med_lat_width_bone_mm / seg_image.GetSpacing()[::-1][





  
  

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        np_inf_sup_axis]) * percent_width_to_crop_height





  
  

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    if bone_crop_distal is True:





  
  

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        bone_distal_idx = np.max(loc_bone[np_inf_sup_axis])





  
  

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        bone_proximal_idx = bone_distal_idx - inf_sup_crop_in_pixels





  
  

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        if bone_proximal_idx &lt; 1:





  
  

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            bone_proximal_idx = 1





  
  

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    elif bone_crop_distal is False:





  
  

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        bone_proximal_idx = np.min(loc_bone[np_inf_sup_axis])





  
  

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        bone_distal_idx = bone_proximal_idx + inf_sup_crop_in_pixels





  
  

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        if bone_distal_idx &gt; seg_array.shape[np_inf_sup_axis]:





  
  

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            bone_distal_idx = seg_array.shape[np_inf_sup_axis] - 1





  
  

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    max_inf_sup_idx = max(bone_distal_idx, bone_proximal_idx)





  
  

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    min_inf_sup_idx = min(bone_distal_idx, bone_proximal_idx)





  
  

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    idx_bone_to_keep = np.where(





  
  

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        (loc_bone[np_inf_sup_axis] &gt; min_inf_sup_idx) &amp; (loc_bone[np_inf_sup_axis] &lt; max_inf_sup_idx))





  
  

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    loc_bone_to_remove = tuple([np.delete(x, idx_bone_to_keep) for x in loc_bone])





  
  

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    seg_array[loc_bone_to_remove] = value_to_reassign





  
  

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    new_seg_image = sitk.GetImageFromArray(seg_array)





  
  

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    new_seg_image.CopyInformation(seg_image)





  
  

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    return new_seg_image





  
  

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def apply_transform_retain_array(image, transform, interpolator=sitk.sitkNearestNeighbor):





  
  

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    &#34;&#34;&#34;





  
  

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    This function will move the actual image in space but keep the underlying array the same. 





  
  

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    So, in x/y/z land the pixels are in a new location, but the actual underlying data array 





  
  

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    is the same. 





  
  

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    Parameters





  
  

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    ----------





  
  

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    image : SimpleITK.Image





  
  

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        Image to be transformed.





  
  

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    transform : SimpleITK.Transform





  
  

  248
  
        Transform to apply





  
  

  249
  
    interpolator : SimpleITK.Interpolator, optional





  
  

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        Interpolator type to use, by default sitk.sitkNearestNeighbor





  
  

  251
  





  
  

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    Returns





  
  

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    -------





  
  

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    SimpleITK.Image





  
  

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        New image after applying the appropriate transform.





  
  

  256
  
    





  
  

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    Notes





  
  

  258
  
    -----





  
  

  259
  
    I have a feeling that this is overkill.





  
  

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    &#34;&#34;&#34;    





  
  

  261
  





  
  

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    inverse_transform = transform.GetInverse()





  
  

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    new_origin = inverse_transform.TransformPoint(image.GetOrigin())





  
  

  264
  





  
  

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    new_x = inverse_transform.TransformPoint(image.TransformIndexToPhysicalPoint((image.GetSize()[0], 0, 0)))





  
  

  266
  
    new_y = inverse_transform.TransformPoint(image.TransformIndexToPhysicalPoint((0, image.GetSize()[1], 0)))





  
  

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    new_z = inverse_transform.TransformPoint(image.TransformIndexToPhysicalPoint((0, 0, image.GetSize()[2])))





  
  

  268
  





  
  

  269
  
    # Create x-axis vector





  
  

  270
  
    new_x_vector = np.asarray(new_x) - np.asarray(new_origin)





  
  

  271
  
    new_x_vector /= np.sqrt(np.sum(np.square(new_x_vector)))





  
  

  272
  
    # Create y-axis vector





  
  

  273
  
    new_y_vector = np.asarray(new_y) - np.asarray(new_origin)





  
  

  274
  
    new_y_vector /= np.sqrt(np.sum(np.square(new_y_vector)))





  
  

  275
  
    # Create z-axis vector





  
  

  276
  
    new_z_vector = np.asarray(new_z) - np.asarray(new_origin)





  
  

  277
  
    new_z_vector /= np.sqrt(np.sum(np.square(new_z_vector)))





  
  

  278
  
    # New image size (shape)





  
  

  279
  
    new_size = image.GetSize()





  
  

  280
  
    # New image spacing 





  
  

  281
  
    new_spacing = image.GetSpacing()





  
  

  282
  
    # Create 3x3 transformation matrix from the x/y/z unit vectors. 





  
  

  283
  
    new_three_by_three = np.zeros((3,3))





  
  

  284
  
    new_three_by_three[:,0] = new_x_vector





  
  

  285
  
    new_three_by_three[:,1] = new_y_vector





  
  

  286
  
    new_three_by_three[:,2] = new_z_vector





  
  

  287
  





  
  

  288
  
    new_image = sitk.Resample(image, 





  
  

  289
  
                              new_size,





  
  

  290
  
                              transform,





  
  

  291
  
                              interpolator,





  
  

  292
  
                              new_origin, 





  
  

  293
  
                              new_spacing,





  
  

  294
  
                              new_three_by_three.flatten().tolist())





  
  

  295
  
    return new_image





  
  

  296
  





  
  

  297
  
def create_vtk_image(





  
  

  298
  
    origin: Optional[int] = [0, 0, 0],





  
  

  299
  
    dimensions: Optional[list] = [20, 20, 20],





  
  

  300
  
    spacing: Optional[float] = [1., 1., 1.],





  
  

  301
  
    scalar: Optional[float] = 20.,





  
  

  302
  
    data: Optional[np.ndarray] = None





  
  

  303
  
):





  
  

  304
  
    &#34;&#34;&#34;





  
  

  305
  
    Function to create a 3D vtkimage from a numpy array





  
  

  306
  
    OR to create a uniform image (all same value)





  
  

  307
  





  
  

  308
  
    Parameters





  
  

  309
  
    ----------





  
  

  310
  
    origin : Optional[int], optional





  
  

  311
  
        X/Y/Z origin of the image, by default [0, 0, 0]





  
  

  312
  
    dimensions : Optional[list], optional





  
  

  313
  
        Size of the image along each dimension, by default [20, 20, 20]





  
  

  314
  
    spacing : Optional[float], optional





  
  

  315
  
        Image spacing along each dimension, by default [1., 1., 1.]





  
  

  316
  
    scalar : Optional[float], optional





  
  

  317
  
        Scalar value to use for a uniform image, by default 20.





  
  

  318
  
    data : Optional[np.ndarray], optional





  
  

  319
  
        Data for a non-uniform image, by default None





  
  

  320
  
    &#34;&#34;&#34;    





  
  

  321
  
    





  
  

  322
  
    if data is None:





  
  

  323
  
        data = np.ones(dimensions) * scalar





  
  

  324
  
    else:





  
  

  325
  
        if len(data.shape) == 3:





  
  

  326
  
            dimensions = data.shape





  
  

  327
  
        else:





  
  

  328
  
            dimensions = [1, 1, 1]





  
  

  329
  
            for idx, dim_size in enumerate(data.shape):





  
  

  330
  
                dimensions[idx] = dim_size





  
  

  331
  
    vtk_array = numpy_to_vtk(data.flatten(order=&#39;F&#39;))





  
  

  332
  
    vtk_array.SetName(&#39;test&#39;)





  
  

  333
  





  
  

  334
  
    # points = vtk.vtkDoubleArray()





  
  

  335
  
    # points.SetName(&#39;test&#39;)





  
  

  336
  
    # points.SetNumberOfComponents(1)





  
  

  337
  
    # points.SetNumberOfTuples(np.product(dimensions))





  
  

  338
  
    # for x in range(dimensions[0]):





  
  

  339
  
    #   for y in range(dimensions[1]):





  
  

  340
  
    #      for z in range(dimensions[2]):





  
  

  341
  
    #         points.SetValue(





  
  

  342
  
    #            (z * dimensions[0] * dimensions[1]) + (x * dimensions[1]) + y,





  
  

  343
  
    #            array[x, y, z]





  
  

  344
  
    #         )





  
  

  345
  





  
  

  346
  
    vtk_image = vtk.vtkImageData()





  
  

  347
  
    vtk_image.SetOrigin(*origin)





  
  

  348
  
    vtk_image.SetDimensions(*dimensions)





  
  

  349
  
    vtk_image.SetSpacing(*spacing)





  
  

  350
  
    vtk_image.GetPointData().SetScalars(vtk_array)            





  
  

  351
  





  
  

  352
  
# 





  
  

  353
  
    return vtk_image</code></pre>





  
  

  354
  
</details>





  
  

  355
  
</section>





  
  

  356
  
<section>





  
  

  357
  
</section>





  
  

  358
  
<section>





  
  

  359
  
</section>





  
  

  360
  
<section>





  
  

  361
  
<h2 class="section-title" id="header-functions">Functions</h2>





  
  

  362
  
<dl>





  
  

  363
  
<dt id="pymskt.image.main.apply_transform_retain_array"><code class="name flex">





  
  

  364
  
<span>def <span class="ident">apply_transform_retain_array</span></span>(<span>image, transform, interpolator=1)</span>





  
  

  365
  
</code></dt>





  
  

  366
  
<dd>





  
  

  367
  
<div class="desc"><p>This function will move the actual image in space but keep the underlying array the same.





  
  

  368
  
So, in x/y/z land the pixels are in a new location, but the actual underlying data array





  
  

  369
  
is the same. </p>





  
  

  370
  
<h2 id="parameters">Parameters</h2>





  
  

  371
  
<dl>





  
  

  372
  
<dt><strong><code>image</code></strong> :&ensp;<code>SimpleITK.Image</code></dt>





  
  

  373
  
<dd>Image to be transformed.</dd>





  
  

  374
  
<dt><strong><code>transform</code></strong> :&ensp;<code>SimpleITK.Transform</code></dt>





  
  

  375
  
<dd>Transform to apply</dd>





  
  

  376
  
<dt><strong><code>interpolator</code></strong> :&ensp;<code>SimpleITK.Interpolator</code>, optional</dt>





  
  

  377
  
<dd>Interpolator type to use, by default sitk.sitkNearestNeighbor</dd>





  
  

  378
  
</dl>





  
  

  379
  
<h2 id="returns">Returns</h2>





  
  

  380
  
<dl>





  
  

  381
  
<dt><code>SimpleITK.Image</code></dt>





  
  

  382
  
<dd>New image after applying the appropriate transform.</dd>





  
  

  383
  
</dl>





  
  

  384
  
<h2 id="notes">Notes</h2>





  
  

  385
  
<p>I have a feeling that this is overkill.</p></div>





  
  

  386
  
<details class="source">





  
  

  387
  
<summary>





  
  

  388
  
<span>Expand source code</span>





  
  

  389
  
</summary>





  
  

  390
  
<pre><code class="python">def apply_transform_retain_array(image, transform, interpolator=sitk.sitkNearestNeighbor):





  
  

  391
  
    &#34;&#34;&#34;





  
  

  392
  
    This function will move the actual image in space but keep the underlying array the same. 





  
  

  393
  
    So, in x/y/z land the pixels are in a new location, but the actual underlying data array 





  
  

  394
  
    is the same. 





  
  

  395
  





  
  

  396
  
    Parameters





  
  

  397
  
    ----------





  
  

  398
  
    image : SimpleITK.Image





  
  

  399
  
        Image to be transformed.





  
  

  400
  
    transform : SimpleITK.Transform





  
  

  401
  
        Transform to apply





  
  

  402
  
    interpolator : SimpleITK.Interpolator, optional





  
  

  403
  
        Interpolator type to use, by default sitk.sitkNearestNeighbor





  
  

  404
  





  
  

  405
  
    Returns





  
  

  406
  
    -------





  
  

  407
  
    SimpleITK.Image





  
  

  408
  
        New image after applying the appropriate transform.





  
  

  409
  
    





  
  

  410
  
    Notes





  
  

  411
  
    -----





  
  

  412
  
    I have a feeling that this is overkill.





  
  

  413
  
    &#34;&#34;&#34;    





  
  

  414
  





  
  

  415
  
    inverse_transform = transform.GetInverse()





  
  

  416
  
    new_origin = inverse_transform.TransformPoint(image.GetOrigin())





  
  

  417
  





  
  

  418
  
    new_x = inverse_transform.TransformPoint(image.TransformIndexToPhysicalPoint((image.GetSize()[0], 0, 0)))





  
  

  419
  
    new_y = inverse_transform.TransformPoint(image.TransformIndexToPhysicalPoint((0, image.GetSize()[1], 0)))





  
  

  420
  
    new_z = inverse_transform.TransformPoint(image.TransformIndexToPhysicalPoint((0, 0, image.GetSize()[2])))





  
  

  421
  





  
  

  422
  
    # Create x-axis vector





  
  

  423
  
    new_x_vector = np.asarray(new_x) - np.asarray(new_origin)





  
  

  424
  
    new_x_vector /= np.sqrt(np.sum(np.square(new_x_vector)))





  
  

  425
  
    # Create y-axis vector





  
  

  426
  
    new_y_vector = np.asarray(new_y) - np.asarray(new_origin)





  
  

  427
  
    new_y_vector /= np.sqrt(np.sum(np.square(new_y_vector)))





  
  

  428
  
    # Create z-axis vector





  
  

  429
  
    new_z_vector = np.asarray(new_z) - np.asarray(new_origin)





  
  

  430
  
    new_z_vector /= np.sqrt(np.sum(np.square(new_z_vector)))





  
  

  431
  
    # New image size (shape)





  
  

  432
  
    new_size = image.GetSize()





  
  

  433
  
    # New image spacing 





  
  

  434
  
    new_spacing = image.GetSpacing()





  
  

  435
  
    # Create 3x3 transformation matrix from the x/y/z unit vectors. 





  
  

  436
  
    new_three_by_three = np.zeros((3,3))





  
  

  437
  
    new_three_by_three[:,0] = new_x_vector





  
  

  438
  
    new_three_by_three[:,1] = new_y_vector





  
  

  439
  
    new_three_by_three[:,2] = new_z_vector





  
  

  440
  





  
  

  441
  
    new_image = sitk.Resample(image, 





  
  

  442
  
                              new_size,





  
  

  443
  
                              transform,





  
  

  444
  
                              interpolator,





  
  

  445
  
                              new_origin, 





  
  

  446
  
                              new_spacing,





  
  

  447
  
                              new_three_by_three.flatten().tolist())





  
  

  448
  
    return new_image</code></pre>





  
  

  449
  
</details>





  
  

  450
  
</dd>





  
  

  451
  
<dt id="pymskt.image.main.binarize_segmentation_image"><code class="name flex">





  
  

  452
  
<span>def <span class="ident">binarize_segmentation_image</span></span>(<span>seg_image, label_idx)</span>





  
  

  453
  
</code></dt>





  
  

  454
  
<dd>





  
  

  455
  
<div class="desc"><p>Return segmentation that is only 0s/1s, with 1s where label_idx is





  
  

  456
  
located in the image. </p>





  
  

  457
  
<h2 id="parameters">Parameters</h2>





  
  

  458
  
<dl>





  
  

  459
  
<dt><strong><code>seg_image</code></strong> :&ensp;<code>SimpleITK.Image</code></dt>





  
  

  460
  
<dd>Segmentation image that contains data we want to binarize</dd>





  
  

  461
  
<dt><strong><code>label_idx</code></strong> :&ensp;<code>int</code></dt>





  
  

  462
  
<dd>Integer/label that we want to extract (binarize) from the





  
  

  463
  
<code>seg_image</code>.</dd>





  
  

  464
  
</dl>





  
  

  465
  
<h2 id="returns">Returns</h2>





  
  

  466
  
<dl>





  
  

  467
  
<dt><code>SimpleITK.Image</code></dt>





  
  

  468
  
<dd>New segmentation image that is binarized.</dd>





  
  

  469
  
</dl></div>





  
  

  470
  
<details class="source">





  
  

  471
  
<summary>





  
  

  472
  
<span>Expand source code</span>





  
  

  473
  
</summary>





  
  

  474
  
<pre><code class="python">def binarize_segmentation_image(seg_image, label_idx):





  
  

  475
  
    &#34;&#34;&#34;





  
  

  476
  
    Return segmentation that is only 0s/1s, with 1s where label_idx is





  
  

  477
  
    located in the image. 





  
  

  478
  





  
  

  479
  
    Parameters





  
  

  480
  
    ----------





  
  

  481
  
    seg_image : SimpleITK.Image





  
  

  482
  
        Segmentation image that contains data we want to binarize





  
  

  483
  
    label_idx : int





  
  

  484
  
        Integer/label that we want to extract (binarize) from the





  
  

  485
  
        `seg_image`.





  
  

  486
  





  
  

  487
  
    Returns





  
  

  488
  
    -------





  
  

  489
  
    SimpleITK.Image





  
  

  490
  
        New segmentation image that is binarized. 





  
  

  491
  
    &#34;&#34;&#34;    





  
  

  492
  
    array = sitk.GetArrayFromImage(seg_image)





  
  

  493
  
    array_ = np.zeros_like(array)





  
  

  494
  
    array_[array == label_idx] = 1





  
  

  495
  
    new_seg_image = sitk.GetImageFromArray(array_)





  
  

  496
  
    new_seg_image.CopyInformation(seg_image)





  
  

  497
  
    return new_seg_image</code></pre>





  
  

  498
  
</details>





  
  

  499
  
</dd>





  
  

  500
  
<dt id="pymskt.image.main.create_vtk_image"><code class="name flex">





  
  

  501
  
<span>def <span class="ident">create_vtk_image</span></span>(<span>origin: Optional[int] = [0, 0, 0], dimensions: Optional[list] = [20, 20, 20], spacing: Optional[float] = [1.0, 1.0, 1.0], scalar: Optional[float] = 20.0, data: Optional[numpy.ndarray] = None)</span>





  
  

  502
  
</code></dt>





  
  

  503
  
<dd>





  
  

  504
  
<div class="desc"><p>Function to create a 3D vtkimage from a numpy array





  
  

  505
  
OR to create a uniform image (all same value)</p>





  
  

  506
  
<h2 id="parameters">Parameters</h2>





  
  

  507
  
<dl>





  
  

  508
  
<dt><strong><code>origin</code></strong> :&ensp;<code>Optional[int]</code>, optional</dt>





  
  

  509
  
<dd>X/Y/Z origin of the image, by default [0, 0, 0]</dd>





  
  

  510
  
<dt><strong><code>dimensions</code></strong> :&ensp;<code>Optional[list]</code>, optional</dt>





  
  

  511
  
<dd>Size of the image along each dimension, by default [20, 20, 20]</dd>





  
  

  512
  
<dt><strong><code>spacing</code></strong> :&ensp;<code>Optional[float]</code>, optional</dt>





  
  

  513
  
<dd>Image spacing along each dimension, by default [1., 1., 1.]</dd>





  
  

  514
  
<dt><strong><code>scalar</code></strong> :&ensp;<code>Optional[float]</code>, optional</dt>





  
  

  515
  
<dd>Scalar value to use for a uniform image, by default 20.</dd>





  
  

  516
  
<dt><strong><code>data</code></strong> :&ensp;<code>Optional[np.ndarray]</code>, optional</dt>





  
  

  517
  
<dd>Data for a non-uniform image, by default None</dd>





  
  

  518
  
</dl></div>





  
  

  519
  
<details class="source">





  
  

  520
  
<summary>





  
  

  521
  
<span>Expand source code</span>





  
  

  522
  
</summary>





  
  

  523
  
<pre><code class="python">def create_vtk_image(





  
  

  524
  
    origin: Optional[int] = [0, 0, 0],





  
  

  525
  
    dimensions: Optional[list] = [20, 20, 20],





  
  

  526
  
    spacing: Optional[float] = [1., 1., 1.],





  
  

  527
  
    scalar: Optional[float] = 20.,





  
  

  528
  
    data: Optional[np.ndarray] = None





  
  

  529
  
):





  
  

  530
  
    &#34;&#34;&#34;





  
  

  531
  
    Function to create a 3D vtkimage from a numpy array





  
  

  532
  
    OR to create a uniform image (all same value)





  
  

  533
  





  
  

  534
  
    Parameters





  
  

  535
  
    ----------





  
  

  536
  
    origin : Optional[int], optional





  
  

  537
  
        X/Y/Z origin of the image, by default [0, 0, 0]





  
  

  538
  
    dimensions : Optional[list], optional





  
  

  539
  
        Size of the image along each dimension, by default [20, 20, 20]





  
  

  540
  
    spacing : Optional[float], optional





  
  

  541
  
        Image spacing along each dimension, by default [1., 1., 1.]





  
  

  542
  
    scalar : Optional[float], optional





  
  

  543
  
        Scalar value to use for a uniform image, by default 20.





  
  

  544
  
    data : Optional[np.ndarray], optional





  
  

  545
  
        Data for a non-uniform image, by default None





  
  

  546
  
    &#34;&#34;&#34;    





  
  

  547
  
    





  
  

  548
  
    if data is None:





  
  

  549
  
        data = np.ones(dimensions) * scalar





  
  

  550
  
    else:





  
  

  551
  
        if len(data.shape) == 3:





  
  

  552
  
            dimensions = data.shape





  
  

  553
  
        else:





  
  

  554
  
            dimensions = [1, 1, 1]





  
  

  555
  
            for idx, dim_size in enumerate(data.shape):





  
  

  556
  
                dimensions[idx] = dim_size





  
  

  557
  
    vtk_array = numpy_to_vtk(data.flatten(order=&#39;F&#39;))





  
  

  558
  
    vtk_array.SetName(&#39;test&#39;)





  
  

  559
  





  
  

  560
  
    # points = vtk.vtkDoubleArray()





  
  

  561
  
    # points.SetName(&#39;test&#39;)





  
  

  562
  
    # points.SetNumberOfComponents(1)





  
  

  563
  
    # points.SetNumberOfTuples(np.product(dimensions))





  
  

  564
  
    # for x in range(dimensions[0]):





  
  

  565
  
    #   for y in range(dimensions[1]):





  
  

  566
  
    #      for z in range(dimensions[2]):





  
  

  567
  
    #         points.SetValue(





  
  

  568
  
    #            (z * dimensions[0] * dimensions[1]) + (x * dimensions[1]) + y,





  
  

  569
  
    #            array[x, y, z]





  
  

  570
  
    #         )





  
  

  571
  





  
  

  572
  
    vtk_image = vtk.vtkImageData()





  
  

  573
  
    vtk_image.SetOrigin(*origin)





  
  

  574
  
    vtk_image.SetDimensions(*dimensions)





  
  

  575
  
    vtk_image.SetSpacing(*spacing)





  
  

  576
  
    vtk_image.GetPointData().SetScalars(vtk_array)            





  
  

  577
  





  
  

  578
  
# 





  
  

  579
  
    return vtk_image</code></pre>





  
  

  580
  
</details>





  
  

  581
  
</dd>





  
  

  582
  
<dt id="pymskt.image.main.crop_bone_based_on_width"><code class="name flex">





  
  

  583
  
<span>def <span class="ident">crop_bone_based_on_width</span></span>(<span>seg_image, bone_idx, np_med_lat_axis=0, np_inf_sup_axis=1, bone_crop_distal=True, value_to_reassign=0, percent_width_to_crop_height=1.0)</span>





  
  

  584
  
</code></dt>





  
  

  585
  
<dd>





  
  

  586
  
<div class="desc"><p>Crop the bone labelmap of a SimpleITK.Image so that it is proportional to the





  
  

  587
  
bones medial/lateral width. </p>





  
  

  588
  
<h2 id="parameters">Parameters</h2>





  
  

  589
  
<dl>





  
  

  590
  
<dt><strong><code>seg_image</code></strong> :&ensp;<code>SimpleITK.Image</code></dt>





  
  

  591
  
<dd>Image to be cropped.</dd>





  
  

  592
  
<dt><strong><code>bone_idx</code></strong> :&ensp;<code>int</code></dt>





  
  

  593
  
<dd>Label_index of the bone to be cropped.</dd>





  
  

  594
  
<dt><strong><code>np_med_lat_axis</code></strong> :&ensp;<code>int</code>, optional</dt>





  
  

  595
  
<dd>Medial/lateral axis, by default 0</dd>





  
  

  596
  
<dt><strong><code>np_inf_sup_axis</code></strong> :&ensp;<code>int</code>, optional</dt>





  
  

  597
  
<dd>Inferior/superir axis, by default 1</dd>





  
  

  598
  
<dt><strong><code>bone_crop_distal</code></strong> :&ensp;<code>bool</code>, optional</dt>





  
  

  599
  
<dd>Boolean of cropping should occur distal or proximally, by default True</dd>





  
  

  600
  
<dt><strong><code>value_to_reassign</code></strong> :&ensp;<code>int</code>, optional</dt>





  
  

  601
  
<dd>Value to replace bone label with, by default 0</dd>





  
  

  602
  
<dt><strong><code>percent_width_to_crop_height</code></strong> :&ensp;<code>float</code>, optional</dt>





  
  

  603
  
<dd>Bone length as a proportion of width, by default 1.0</dd>





  
  

  604
  
</dl>





  
  

  605
  
<h2 id="returns">Returns</h2>





  
  

  606
  
<dl>





  
  

  607
  
<dt><code>SimpleITK.Image</code></dt>





  
  

  608
  
<dd>Image after bone is cropped as a proportion of the bone's width.</dd>





  
  

  609
  
</dl></div>





  
  

  610
  
<details class="source">





  
  

  611
  
<summary>





  
  

  612
  
<span>Expand source code</span>





  
  

  613
  
</summary>





  
  

  614
  
<pre><code class="python">def crop_bone_based_on_width(seg_image,





  
  

  615
  
                             bone_idx,





  
  

  616
  
                             np_med_lat_axis=0,





  
  

  617
  
                             np_inf_sup_axis=1,





  
  

  618
  
                             bone_crop_distal=True,





  
  

  619
  
                             value_to_reassign=0,





  
  

  620
  
                             percent_width_to_crop_height=1.0):





  
  

  621
  
    &#34;&#34;&#34;





  
  

  622
  
    Crop the bone labelmap of a SimpleITK.Image so that it is proportional to the





  
  

  623
  
    bones medial/lateral width. 





  
  

  624
  





  
  

  625
  
    Parameters





  
  

  626
  
    ----------





  
  

  627
  
    seg_image : SimpleITK.Image





  
  

  628
  
        Image to be cropped. 





  
  

  629
  
    bone_idx : int





  
  

  630
  
        Label_index of the bone to be cropped. 





  
  

  631
  
    np_med_lat_axis : int, optional





  
  

  632
  
        Medial/lateral axis, by default 0





  
  

  633
  
    np_inf_sup_axis : int, optional





  
  

  634
  
        Inferior/superir axis, by default 1





  
  

  635
  
    bone_crop_distal : bool, optional





  
  

  636
  
        Boolean of cropping should occur distal or proximally, by default True





  
  

  637
  
    value_to_reassign : int, optional





  
  

  638
  
        Value to replace bone label with, by default 0





  
  

  639
  
    percent_width_to_crop_height : float, optional





  
  

  640
  
        Bone length as a proportion of width, by default 1.0





  
  

  641
  





  
  

  642
  
    Returns





  
  

  643
  
    -------





  
  

  644
  
    SimpleITK.Image





  
  

  645
  
        Image after bone is cropped as a proportion of the bone&#39;s width. 





  
  

  646
  
    &#34;&#34;&#34;    





  
  

  647
  
    seg_array = sitk.GetArrayFromImage(seg_image)





  
  

  648
  
    loc_bone = np.where(seg_array == bone_idx)





  
  

  649
  
    med_lat_width_bone_mm = (np.max(loc_bone[np_med_lat_axis]) - np.min(loc_bone[np_med_lat_axis])) * \





  
  

  650
  
                            seg_image.GetSpacing()[::-1][np_med_lat_axis]





  
  

  651
  
    inf_sup_crop_in_pixels = (med_lat_width_bone_mm / seg_image.GetSpacing()[::-1][





  
  

  652
  
        np_inf_sup_axis]) * percent_width_to_crop_height





  
  

  653
  
    if bone_crop_distal is True:





  
  

  654
  
        bone_distal_idx = np.max(loc_bone[np_inf_sup_axis])





  
  

  655
  
        bone_proximal_idx = bone_distal_idx - inf_sup_crop_in_pixels





  
  

  656
  
        if bone_proximal_idx &lt; 1:





  
  

  657
  
            bone_proximal_idx = 1





  
  

  658
  





  
  

  659
  
    elif bone_crop_distal is False:





  
  

  660
  
        bone_proximal_idx = np.min(loc_bone[np_inf_sup_axis])





  
  

  661
  
        bone_distal_idx = bone_proximal_idx + inf_sup_crop_in_pixels





  
  

  662
  
        if bone_distal_idx &gt; seg_array.shape[np_inf_sup_axis]:





  
  

  663
  
            bone_distal_idx = seg_array.shape[np_inf_sup_axis] - 1





  
  

  664
  





  
  

  665
  
    max_inf_sup_idx = max(bone_distal_idx, bone_proximal_idx)





  
  

  666
  
    min_inf_sup_idx = min(bone_distal_idx, bone_proximal_idx)





  
  

  667
  





  
  

  668
  
    idx_bone_to_keep = np.where(





  
  

  669
  
        (loc_bone[np_inf_sup_axis] &gt; min_inf_sup_idx) &amp; (loc_bone[np_inf_sup_axis] &lt; max_inf_sup_idx))





  
  

  670
  
    loc_bone_to_remove = tuple([np.delete(x, idx_bone_to_keep) for x in loc_bone])





  
  

  671
  





  
  

  672
  
    seg_array[loc_bone_to_remove] = value_to_reassign





  
  

  673
  





  
  

  674
  
    new_seg_image = sitk.GetImageFromArray(seg_array)





  
  

  675
  
    new_seg_image.CopyInformation(seg_image)





  
  

  676
  





  
  

  677
  
    return new_seg_image</code></pre>





  
  

  678
  
</details>





  
  

  679
  
</dd>





  
  

  680
  
<dt id="pymskt.image.main.read_nrrd"><code class="name flex">





  
  

  681
  
<span>def <span class="ident">read_nrrd</span></span>(<span>path, set_origin_zero=False)</span>





  
  

  682
  
</code></dt>





  
  

  683
  
<dd>





  
  

  684
  
<div class="desc"><p>Read NRRD image file into vtk. Enables usage of marching cubes





  
  

  685
  
and other functions that work on image data.</p>





  
  

  686
  
<h2 id="parameters">Parameters</h2>





  
  

  687
  
<dl>





  
  

  688
  
<dt><strong><code>path</code></strong> :&ensp;<code>str</code></dt>





  
  

  689
  
<dd>Path to <code>.nrrd</code> medical image to read in.</dd>





  
  

  690
  
<dt><strong><code>set_origin_zero</code></strong> :&ensp;<code>bool</code>, optional</dt>





  
  

  691
  
<dd>Bool to determine if origin should be set to zeros, by default False</dd>





  
  

  692
  
</dl>





  
  

  693
  
<h2 id="returns">Returns</h2>





  
  

  694
  
<dl>





  
  

  695
  
<dt><code>vtk.Filter</code></dt>





  
  

  696
  
<dd>End of VTK filter pipeline.</dd>





  
  

  697
  
</dl></div>





  
  

  698
  
<details class="source">





  
  

  699
  
<summary>





  
  

  700
  
<span>Expand source code</span>





  
  

  701
  
</summary>





  
  

  702
  
<pre><code class="python">def read_nrrd(path, set_origin_zero=False):





  
  

  703
  
    &#34;&#34;&#34;





  
  

  704
  
    Read NRRD image file into vtk. Enables usage of marching cubes





  
  

  705
  
    and other functions that work on image data.





  
  

  706
  





  
  

  707
  
    Parameters





  
  

  708
  
    ----------





  
  

  709
  
    path : str





  
  

  710
  
        Path to `.nrrd` medical image to read in. 





  
  

  711
  
    set_origin_zero : bool, optional





  
  

  712
  
        Bool to determine if origin should be set to zeros, by default False





  
  

  713
  





  
  

  714
  
    Returns





  
  

  715
  
    -------





  
  

  716
  
    vtk.Filter





  
  

  717
  
        End of VTK filter pipeline. 





  
  

  718
  
    &#34;&#34;&#34;    





  
  

  719
  





  
  

  720
  
    image_reader = vtk.vtkNrrdReader()





  
  

  721
  
    image_reader.SetFileName(path)





  
  

  722
  
    image_reader.Update()





  
  

  723
  
    if set_origin_zero is True:





  
  

  724
  
        change_origin = set_vtk_image_origin(image_reader, new_origin=(0, 0, 0))





  
  

  725
  
        return change_origin





  
  

  726
  
    elif set_origin_zero is False:





  
  

  727
  
        return image_reader</code></pre>





  
  

  728
  
</details>





  
  

  729
  
</dd>





  
  

  730
  
<dt id="pymskt.image.main.set_seg_border_to_zeros"><code class="name flex">





  
  

  731
  
<span>def <span class="ident">set_seg_border_to_zeros</span></span>(<span>seg_image, border_size=1)</span>





  
  

  732
  
</code></dt>





  
  

  733
  
<dd>





  
  

  734
  
<div class="desc"><p>Utility function to ensure that all segmentations are "closed" after marching cubes.





  
  

  735
  
If the segmentation extends to the edges of the image then the surface wont be closed





  
  

  736
  
at the places it touches the edges. </p>





  
  

  737
  
<h2 id="parameters">Parameters</h2>





  
  

  738
  
<dl>





  
  

  739
  
<dt><strong><code>seg_image</code></strong> :&ensp;<code>SimpleITK.Image</code></dt>





  
  

  740
  
<dd>Image of a segmentation.</dd>





  
  

  741
  
<dt><strong><code>border_size</code></strong> :&ensp;<code>int</code>, optional</dt>





  
  

  742
  
<dd>The size of the border to set around the edges of the 3D image, by default 1</dd>





  
  

  743
  
</dl>





  
  

  744
  
<h2 id="returns">Returns</h2>





  
  

  745
  
<dl>





  
  

  746
  
<dt><code>SimpleITK.Image</code></dt>





  
  

  747
  
<dd>The image with border set to 0 (background).</dd>





  
  

  748
  
</dl></div>





  
  

  749
  
<details class="source">





  
  

  750
  
<summary>





  
  

  751
  
<span>Expand source code</span>





  
  

  752
  
</summary>





  
  

  753
  
<pre><code class="python">def set_seg_border_to_zeros(seg_image,





  
  

  754
  
                            border_size=1):





  
  

  755
  
    &#34;&#34;&#34;





  
  

  756
  
    Utility function to ensure that all segmentations are &#34;closed&#34; after marching cubes. 





  
  

  757
  
    If the segmentation extends to the edges of the image then the surface wont be closed 





  
  

  758
  
    at the places it touches the edges. 





  
  

  759
  





  
  

  760
  
    Parameters





  
  

  761
  
    ----------





  
  

  762
  
    seg_image : SimpleITK.Image





  
  

  763
  
        Image of a segmentation. 





  
  

  764
  
    border_size : int, optional





  
  

  765
  
        The size of the border to set around the edges of the 3D image, by default 1





  
  

  766
  





  
  

  767
  
    Returns





  
  

  768
  
    -------





  
  

  769
  
    SimpleITK.Image





  
  

  770
  
        The image with border set to 0 (background). 





  
  

  771
  
    &#34;&#34;&#34;    





  
  

  772
  





  
  

  773
  
    seg_array = sitk.GetArrayFromImage(seg_image)





  
  

  774
  
    new_seg_array = np.zeros_like(seg_array)





  
  

  775
  
    new_seg_array[border_size:-border_size, border_size:-border_size, border_size:-border_size] = seg_array[border_size:-border_size, border_size:-border_size, border_size:-border_size]





  
  

  776
  
    new_seg_image = sitk.GetImageFromArray(new_seg_array)





  
  

  777
  
    new_seg_image.CopyInformation(seg_image)





  
  

  778
  
    return new_seg_image</code></pre>





  
  

  779
  
</details>





  
  

  780
  
</dd>





  
  

  781
  
<dt id="pymskt.image.main.set_vtk_image_origin"><code class="name flex">





  
  

  782
  
<span>def <span class="ident">set_vtk_image_origin</span></span>(<span>vtk_image, new_origin=(0, 0, 0))</span>





  
  

  783
  
</code></dt>





  
  

  784
  
<dd>





  
  

  785
  
<div class="desc"><p>Reset the origin of a <code>vtk_image</code></p>





  
  

  786
  
<h2 id="parameters">Parameters</h2>





  
  

  787
  
<dl>





  
  

  788
  
<dt><strong><code>vtk_image</code></strong> :&ensp;<code>vtk.image</code></dt>





  
  

  789
  
<dd>VTK image that we want to change the origin of.</dd>





  
  

  790
  
<dt><strong><code>new_origin</code></strong> :&ensp;<code>tuple</code>, optional</dt>





  
  

  791
  
<dd>New origin to asign to <code>vtk_image</code>, by default (0, 0, 0)</dd>





  
  

  792
  
</dl>





  
  

  793
  
<h2 id="returns">Returns</h2>





  
  

  794
  
<dl>





  
  

  795
  
<dt><code>vtk.Filter</code></dt>





  
  

  796
  
<dd>End of VTK filter pipeline after applying origin change.</dd>





  
  

  797
  
</dl></div>





  
  

  798
  
<details class="source">





  
  

  799
  
<summary>





  
  

  800
  
<span>Expand source code</span>





  
  

  801
  
</summary>





  
  

  802
  
<pre><code class="python">def set_vtk_image_origin(vtk_image, new_origin=(0, 0, 0)):





  
  

  803
  
    &#34;&#34;&#34;





  
  

  804
  
    Reset the origin of a `vtk_image`





  
  

  805
  





  
  

  806
  
    Parameters





  
  

  807
  
    ----------





  
  

  808
  
    vtk_image : vtk.image





  
  

  809
  
        VTK image that we want to change the origin of. 





  
  

  810
  
    new_origin : tuple, optional





  
  

  811
  
        New origin to asign to `vtk_image`, by default (0, 0, 0)





  
  

  812
  





  
  

  813
  
    Returns





  
  

  814
  
    -------





  
  

  815
  
    vtk.Filter





  
  

  816
  
        End of VTK filter pipeline after applying origin change. 





  
  

  817
  
    &#34;&#34;&#34;    





  
  

  818
  





  
  

  819
  
    change_origin = vtk.vtkImageChangeInformation()





  
  

  820
  
    change_origin.SetInputConnection(vtk_image.GetOutputPort())





  
  

  821
  
    change_origin.SetOutputOrigin(new_origin)





  
  

  822
  
    change_origin.Update()





  
  

  823
  
    return change_origin</code></pre>





  
  

  824
  
</details>





  
  

  825
  
</dd>





  
  

  826
  
<dt id="pymskt.image.main.smooth_image"><code class="name flex">





  
  

  827
  
<span>def <span class="ident">smooth_image</span></span>(<span>image, label_idx, variance=1.0)</span>





  
  

  828
  
</code></dt>





  
  

  829
  
<dd>





  
  

  830
  
<div class="desc"><p>Smooth a single label in a SimpleITK image. Used as pre-processing for





  
  

  831
  
bones/cartilage before applying marching cubes. Helps obtain smooth surfaces. </p>





  
  

  832
  
<h2 id="parameters">Parameters</h2>





  
  

  833
  
<dl>





  
  

  834
  
<dt><strong><code>image</code></strong> :&ensp;<code>SimpleITK.Image</code></dt>





  
  

  835
  
<dd>Image to be smoothed.</dd>





  
  

  836
  
<dt><strong><code>label_idx</code></strong> :&ensp;<code>int</code></dt>





  
  

  837
  
<dd>Integer of the tissue of interest to be smoothed in the image.</dd>





  
  

  838
  
<dt><strong><code>variance</code></strong> :&ensp;<code>float</code>, optional</dt>





  
  

  839
  
<dd>The size of the smoothing, by default 1.0</dd>





  
  

  840
  
</dl>





  
  

  841
  
<h2 id="returns">Returns</h2>





  
  

  842
  
<dl>





  
  

  843
  
<dt><code>SimpleITK.Image</code></dt>





  
  

  844
  
<dd>Image of only the label (tissue) of interest after being smoothed.</dd>





  
  

  845
  
</dl></div>





  
  

  846
  
<details class="source">





  
  

  847
  
<summary>





  
  

  848
  
<span>Expand source code</span>





  
  

  849
  
</summary>





  
  

  850
  
<pre><code class="python">def smooth_image(image, label_idx, variance=1.0):





  
  

  851
  
    &#34;&#34;&#34;





  
  

  852
  
    Smooth a single label in a SimpleITK image. Used as pre-processing for 





  
  

  853
  
    bones/cartilage before applying marching cubes. Helps obtain smooth surfaces. 





  
  

  854
  





  
  

  855
  
    Parameters





  
  

  856
  
    ----------





  
  

  857
  
    image : SimpleITK.Image





  
  

  858
  
        Image to be smoothed. 





  
  

  859
  
    label_idx : int





  
  

  860
  
        Integer of the tissue of interest to be smoothed in the image. 





  
  

  861
  
    variance : float, optional





  
  

  862
  
        The size of the smoothing, by default 1.0





  
  

  863
  





  
  

  864
  
    Returns





  
  

  865
  
    -------





  
  

  866
  
    SimpleITK.Image





  
  

  867
  
        Image of only the label (tissue) of interest after being smoothed. 





  
  

  868
  
    &#34;&#34;&#34;    





  
  

  869
  
    new_image = binarize_segmentation_image(image, label_idx)





  
  

  870
  





  
  

  871
  
    new_image = sitk.Cast(new_image, sitk.sitkFloat32)





  
  

  872
  





  
  

  873
  
    gauss_filter = sitk.DiscreteGaussianImageFilter()





  
  

  874
  
    gauss_filter.SetVariance(variance)





  
  

  875
  
    #     gauss_filter.SetUseImageSpacingOn





  
  

  876
  
    gauss_filter.SetUseImageSpacing(True)





  
  

  877
  
    filtered_new_image = gauss_filter.Execute(new_image)





  
  

  878
  





  
  

  879
  
    return filtered_new_image</code></pre>





  
  

  880
  
</details>





  
  

  881
  
</dd>





  
  

  882
  
</dl>





  
  

  883
  
</section>





  
  

  884
  
<section>





  
  

  885
  
</section>





  
  

  886
  
</article>





  
  

  887
  
<nav id="sidebar">





  
  

  888
  
<h1>Index</h1>





  
  

  889
  
<div class="toc">





  
  

  890
  
<ul></ul>





  
  

  891
  
</div>





  
  

  892
  
<ul id="index">





  
  

  893
  
<li><h3>Super-module</h3>





  
  

  894
  
<ul>





  
  

  895
  
<li><code><a title="pymskt.image" href="index.html">pymskt.image</a></code></li>





  
  

  896
  
</ul>





  
  

  897
  
</li>





  
  

  898
  
<li><h3><a href="#header-functions">Functions</a></h3>





  
  

  899
  
<ul class="">





  
  

  900
  
<li><code><a title="pymskt.image.main.apply_transform_retain_array" href="#pymskt.image.main.apply_transform_retain_array">apply_transform_retain_array</a></code></li>





  
  

  901
  
<li><code><a title="pymskt.image.main.binarize_segmentation_image" href="#pymskt.image.main.binarize_segmentation_image">binarize_segmentation_image</a></code></li>





  
  

  902
  
<li><code><a title="pymskt.image.main.create_vtk_image" href="#pymskt.image.main.create_vtk_image">create_vtk_image</a></code></li>





  
  

  903
  
<li><code><a title="pymskt.image.main.crop_bone_based_on_width" href="#pymskt.image.main.crop_bone_based_on_width">crop_bone_based_on_width</a></code></li>





  
  

  904
  
<li><code><a title="pymskt.image.main.read_nrrd" href="#pymskt.image.main.read_nrrd">read_nrrd</a></code></li>





  
  

  905
  
<li><code><a title="pymskt.image.main.set_seg_border_to_zeros" href="#pymskt.image.main.set_seg_border_to_zeros">set_seg_border_to_zeros</a></code></li>





  
  

  906
  
<li><code><a title="pymskt.image.main.set_vtk_image_origin" href="#pymskt.image.main.set_vtk_image_origin">set_vtk_image_origin</a></code></li>





  
  

  907
  
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