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<article id="content">
<header>
<h1 class="title">Module <code>pymskt.mesh.createMesh</code></h1>
</header>
<section id="section-intro">
<details class="source">
<summary>
<span>Expand source code</span>
</summary>
<pre><code class="python">import os
import vtk
import SimpleITK as sitk
import pymskt.image as msktimage
import pymskt.mesh.meshTransform as meshTransform
from pymskt.utils import safely_delete_tmp_file
def discrete_marching_cubes(vtk_image_reader,
n_labels=1,
start_label=1,
end_label=1,
compute_normals_on=True,
return_polydata=True
):
"""
Compute dmc on segmentation image.
Creates a surface mesh (polydata) that closely covers binary (discrete) segmentations.
Parameters
----------
vtk_image_reader : vtk.Filter
VTK Filter pipeline to apply discrete marching cubes to.
n_labels : int, optional
Number of labes to create mesh for, by default 1
start_label : int, optional
Starting index of labels to mesh, by default 1
end_label : int, optional
Ending index of labels to mesh, by default 1
compute_normals_on : bool, optional
Calculate normals to surface, by default True
return_polydata : bool, optional
Whether to return a vtk.polydata or not (`vtk.Filter` pipeline instead), by default True
Returns
-------
vtk.Filter Pipeline
Returns a pipeline which more functions can be chained too - this improves performance.
OR
vtk.Polydata
Returns a polydata (surface mesh).
"""
dmc = vtk.vtkDiscreteMarchingCubes()
dmc.SetInputConnection(vtk_image_reader.GetOutputPort())
if compute_normals_on is True:
dmc.ComputeNormalsOn()
dmc.GenerateValues(n_labels, start_label, end_label)
dmc.Update()
if return_polydata is True:
return dmc.GetOutput()
elif return_polydata is False:
return dmc
def continuous_marching_cubes(vtk_image_reader,
threshold=0.5,
compute_normals_on=True,
compute_gradients_on=True,
return_polydata=True):
"""
- Compute a continuous marching cubes on a segmentation mask.
- Enables defining the surface based on a contour set to a floating point cutoff.
Parameters
----------
vtk_image_reader : vtk.Filter
This is the output of a `vtk.Filter` from a previous step. E.g., output of pymskt.image.read_nrrd().
threshold : float, optional
Floating point value to create surface mesh, by default 0.5
compute_normals_on : bool, optional
Whether or not to compute surface normals for mesh, by default True
compute_gradients_on : bool, optional
Whether or not to compute gradients over mesh surface, by default True
return_polydata : bool, optional
Whether to return a vtk.polydata or not (VTK filter pipeline instead e.g., `mc`), by default True
Returns
-------
vtk.Filter Pipeline
Returns a pipeline which more functions can be chained too - this improves performance.
OR
vtk.Polydata
Returns a polydata (surface mesh).
"""
mc = vtk.vtkMarchingContourFilter()
mc.SetInputConnection(vtk_image_reader.GetOutputPort())
if compute_normals_on is True:
mc.ComputeNormalsOn()
elif compute_normals_on is False:
mc.ComputeNormalsOff()
if compute_gradients_on is True:
mc.ComputeGradientsOn()
elif compute_gradients_on is False:
mc.ComputeGradientsOff()
mc.SetValue(0, threshold)
mc.Update()
if return_polydata is True:
mesh = mc.GetOutput()
return mesh
elif return_polydata is False:
return mc
def create_surface_mesh(seg_image,
label_idx,
image_smooth_var,
loc_tmp_save='/tmp',
tmp_filename='temp_smoothed_bone.nrrd',
copy_image_transform=True,
mc_threshold=0.5,
filter_binary_image=True):
"""
Create surface mesh.
Option to filter binary image to get smoother surface representation.
Parameters
----------
seg_image : SimpleITK.Image
Segmentation image to be filtered and meshed with marching cubes.
label_idx : int
What anatomical label to be meshed.
image_smooth_var : float
Variance to apply a gaussian smoothing function to.
loc_tmp_save : str, optional
Location to save temporary files for passing SimpleITK.Image to vtk functions, by default '/tmp'
tmp_filename : str, optional
Filename of saved temporary file, by default 'temp_smoothed_bone.nrrd'
copy_image_transform : bool, optional
Whether or not to apply image transform to final mesh or to leave it at origin, by default True
mc_threshold : float, optional
What floating point value to create surface mesh at, by default 0.5
filter_binary_image : bool, optional
Should the binary image be filtered (smoothed) or not.
Returns
-------
vtk.Polydata
Surface mesh created using a continuous cutoff `mc_threshold` after applying
gaussian smoothing with variance = `image_smooth_var`.
"""
# Set border of segmentation to 0 so that segs are all closed.
seg_image = msktimage.set_seg_border_to_zeros(seg_image, border_size=1)
if filter_binary_image is True:
# smooth/filter the image to get a better surface.
seg_image = msktimage.smooth_image(seg_image, label_idx, image_smooth_var)
else:
seg_image = msktimage.binarize_segmentation_image(seg_image, label_idx)
# save filtered image to disk so can read it in using vtk nrrd reader
sitk.WriteImage(seg_image, os.path.join(loc_tmp_save, tmp_filename))
smoothed_nrrd_reader = msktimage.read_nrrd(os.path.join(loc_tmp_save, tmp_filename),
set_origin_zero=True)
# create the mesh using continuous marching cubes applied to the smoothed binary image.
smooth_mesh = continuous_marching_cubes(smoothed_nrrd_reader, threshold=mc_threshold)
if copy_image_transform is True:
# copy image transofrm to the image to the mesh so that when viewed (e.g. in 3D Slicer) it is aligned with image
smooth_mesh = meshTransform.copy_image_transform_to_mesh(smooth_mesh, seg_image)
# Delete tmp files
safely_delete_tmp_file(loc_tmp_save,
tmp_filename)
return smooth_mesh</code></pre>
</details>
</section>
<section>
</section>
<section>
</section>
<section>
<h2 class="section-title" id="header-functions">Functions</h2>
<dl>
<dt id="pymskt.mesh.createMesh.continuous_marching_cubes"><code class="name flex">
<span>def <span class="ident">continuous_marching_cubes</span></span>(<span>vtk_image_reader, threshold=0.5, compute_normals_on=True, compute_gradients_on=True, return_polydata=True)</span>
</code></dt>
<dd>
<div class="desc"><ul>
<li>Compute a continuous marching cubes on a segmentation mask. </li>
<li>Enables defining the surface based on a contour set to a floating point cutoff. </li>
</ul>
<h2 id="parameters">Parameters</h2>
<dl>
<dt><strong><code>vtk_image_reader</code></strong> : <code>vtk.Filter</code></dt>
<dd>This is the output of a <code>vtk.Filter</code> from a previous step. E.g., output of pymskt.image.read_nrrd().</dd>
<dt><strong><code>threshold</code></strong> : <code>float</code>, optional</dt>
<dd>Floating point value to create surface mesh, by default 0.5</dd>
<dt><strong><code>compute_normals_on</code></strong> : <code>bool</code>, optional</dt>
<dd>Whether or not to compute surface normals for mesh, by default True</dd>
<dt><strong><code>compute_gradients_on</code></strong> : <code>bool</code>, optional</dt>
<dd>Whether or not to compute gradients over mesh surface, by default True</dd>
<dt><strong><code>return_polydata</code></strong> : <code>bool</code>, optional</dt>
<dd>Whether to return a vtk.polydata or not (VTK filter pipeline instead e.g., <code>mc</code>), by default True</dd>
</dl>
<h2 id="returns">Returns</h2>
<dl>
<dt><code>vtk.Filter Pipeline</code></dt>
<dd>Returns a pipeline which more functions can be chained too - this improves performance.</dd>
<dt><code>OR</code></dt>
<dd> </dd>
<dt><code>vtk.Polydata</code></dt>
<dd>Returns a polydata (surface mesh).</dd>
</dl></div>
<details class="source">
<summary>
<span>Expand source code</span>
</summary>
<pre><code class="python">def continuous_marching_cubes(vtk_image_reader,
threshold=0.5,
compute_normals_on=True,
compute_gradients_on=True,
return_polydata=True):
"""
- Compute a continuous marching cubes on a segmentation mask.
- Enables defining the surface based on a contour set to a floating point cutoff.
Parameters
----------
vtk_image_reader : vtk.Filter
This is the output of a `vtk.Filter` from a previous step. E.g., output of pymskt.image.read_nrrd().
threshold : float, optional
Floating point value to create surface mesh, by default 0.5
compute_normals_on : bool, optional
Whether or not to compute surface normals for mesh, by default True
compute_gradients_on : bool, optional
Whether or not to compute gradients over mesh surface, by default True
return_polydata : bool, optional
Whether to return a vtk.polydata or not (VTK filter pipeline instead e.g., `mc`), by default True
Returns
-------
vtk.Filter Pipeline
Returns a pipeline which more functions can be chained too - this improves performance.
OR
vtk.Polydata
Returns a polydata (surface mesh).
"""
mc = vtk.vtkMarchingContourFilter()
mc.SetInputConnection(vtk_image_reader.GetOutputPort())
if compute_normals_on is True:
mc.ComputeNormalsOn()
elif compute_normals_on is False:
mc.ComputeNormalsOff()
if compute_gradients_on is True:
mc.ComputeGradientsOn()
elif compute_gradients_on is False:
mc.ComputeGradientsOff()
mc.SetValue(0, threshold)
mc.Update()
if return_polydata is True:
mesh = mc.GetOutput()
return mesh
elif return_polydata is False:
return mc</code></pre>
</details>
</dd>
<dt id="pymskt.mesh.createMesh.create_surface_mesh"><code class="name flex">
<span>def <span class="ident">create_surface_mesh</span></span>(<span>seg_image, label_idx, image_smooth_var, loc_tmp_save='/tmp', tmp_filename='temp_smoothed_bone.nrrd', copy_image_transform=True, mc_threshold=0.5, filter_binary_image=True)</span>
</code></dt>
<dd>
<div class="desc"><p>Create surface mesh.
Option to filter binary image to get smoother surface representation.</p>
<h2 id="parameters">Parameters</h2>
<dl>
<dt><strong><code>seg_image</code></strong> : <code>SimpleITK.Image</code></dt>
<dd>Segmentation image to be filtered and meshed with marching cubes.</dd>
<dt><strong><code>label_idx</code></strong> : <code>int</code></dt>
<dd>What anatomical label to be meshed.</dd>
<dt><strong><code>image_smooth_var</code></strong> : <code>float</code></dt>
<dd>Variance to apply a gaussian smoothing function to.</dd>
<dt><strong><code>loc_tmp_save</code></strong> : <code>str</code>, optional</dt>
<dd>Location to save temporary files for passing SimpleITK.Image to vtk functions, by default '/tmp'</dd>
<dt><strong><code>tmp_filename</code></strong> : <code>str</code>, optional</dt>
<dd>Filename of saved temporary file, by default 'temp_smoothed_bone.nrrd'</dd>
<dt><strong><code>copy_image_transform</code></strong> : <code>bool</code>, optional</dt>
<dd>Whether or not to apply image transform to final mesh or to leave it at origin, by default True</dd>
<dt><strong><code>mc_threshold</code></strong> : <code>float</code>, optional</dt>
<dd>What floating point value to create surface mesh at, by default 0.5</dd>
<dt><strong><code>filter_binary_image</code></strong> : <code>bool</code>, optional</dt>
<dd>Should the binary image be filtered (smoothed) or not.</dd>
</dl>
<h2 id="returns">Returns</h2>
<dl>
<dt><code>vtk.Polydata</code></dt>
<dd>Surface mesh created using a continuous cutoff <code>mc_threshold</code> after applying
gaussian smoothing with variance = <code>image_smooth_var</code>.</dd>
</dl></div>
<details class="source">
<summary>
<span>Expand source code</span>
</summary>
<pre><code class="python">def create_surface_mesh(seg_image,
label_idx,
image_smooth_var,
loc_tmp_save='/tmp',
tmp_filename='temp_smoothed_bone.nrrd',
copy_image_transform=True,
mc_threshold=0.5,
filter_binary_image=True):
"""
Create surface mesh.
Option to filter binary image to get smoother surface representation.
Parameters
----------
seg_image : SimpleITK.Image
Segmentation image to be filtered and meshed with marching cubes.
label_idx : int
What anatomical label to be meshed.
image_smooth_var : float
Variance to apply a gaussian smoothing function to.
loc_tmp_save : str, optional
Location to save temporary files for passing SimpleITK.Image to vtk functions, by default '/tmp'
tmp_filename : str, optional
Filename of saved temporary file, by default 'temp_smoothed_bone.nrrd'
copy_image_transform : bool, optional
Whether or not to apply image transform to final mesh or to leave it at origin, by default True
mc_threshold : float, optional
What floating point value to create surface mesh at, by default 0.5
filter_binary_image : bool, optional
Should the binary image be filtered (smoothed) or not.
Returns
-------
vtk.Polydata
Surface mesh created using a continuous cutoff `mc_threshold` after applying
gaussian smoothing with variance = `image_smooth_var`.
"""
# Set border of segmentation to 0 so that segs are all closed.
seg_image = msktimage.set_seg_border_to_zeros(seg_image, border_size=1)
if filter_binary_image is True:
# smooth/filter the image to get a better surface.
seg_image = msktimage.smooth_image(seg_image, label_idx, image_smooth_var)
else:
seg_image = msktimage.binarize_segmentation_image(seg_image, label_idx)
# save filtered image to disk so can read it in using vtk nrrd reader
sitk.WriteImage(seg_image, os.path.join(loc_tmp_save, tmp_filename))
smoothed_nrrd_reader = msktimage.read_nrrd(os.path.join(loc_tmp_save, tmp_filename),
set_origin_zero=True)
# create the mesh using continuous marching cubes applied to the smoothed binary image.
smooth_mesh = continuous_marching_cubes(smoothed_nrrd_reader, threshold=mc_threshold)
if copy_image_transform is True:
# copy image transofrm to the image to the mesh so that when viewed (e.g. in 3D Slicer) it is aligned with image
smooth_mesh = meshTransform.copy_image_transform_to_mesh(smooth_mesh, seg_image)
# Delete tmp files
safely_delete_tmp_file(loc_tmp_save,
tmp_filename)
return smooth_mesh</code></pre>
</details>
</dd>
<dt id="pymskt.mesh.createMesh.discrete_marching_cubes"><code class="name flex">
<span>def <span class="ident">discrete_marching_cubes</span></span>(<span>vtk_image_reader, n_labels=1, start_label=1, end_label=1, compute_normals_on=True, return_polydata=True)</span>
</code></dt>
<dd>
<div class="desc"><p>Compute dmc on segmentation image.
Creates a surface mesh (polydata) that closely covers binary (discrete) segmentations.</p>
<h2 id="parameters">Parameters</h2>
<dl>
<dt><strong><code>vtk_image_reader</code></strong> : <code>vtk.Filter</code></dt>
<dd>VTK Filter pipeline to apply discrete marching cubes to.</dd>
<dt><strong><code>n_labels</code></strong> : <code>int</code>, optional</dt>
<dd>Number of labes to create mesh for, by default 1</dd>
<dt><strong><code>start_label</code></strong> : <code>int</code>, optional</dt>
<dd>Starting index of labels to mesh, by default 1</dd>
<dt><strong><code>end_label</code></strong> : <code>int</code>, optional</dt>
<dd>Ending index of labels to mesh, by default 1</dd>
<dt><strong><code>compute_normals_on</code></strong> : <code>bool</code>, optional</dt>
<dd>Calculate normals to surface, by default True</dd>
<dt><strong><code>return_polydata</code></strong> : <code>bool</code>, optional</dt>
<dd>Whether to return a vtk.polydata or not (<code>vtk.Filter</code> pipeline instead), by default True</dd>
</dl>
<h2 id="returns">Returns</h2>
<dl>
<dt><code>vtk.Filter Pipeline</code></dt>
<dd>Returns a pipeline which more functions can be chained too - this improves performance.</dd>
<dt><code>OR</code></dt>
<dd> </dd>
<dt><code>vtk.Polydata</code></dt>
<dd>Returns a polydata (surface mesh).</dd>
</dl></div>
<details class="source">
<summary>
<span>Expand source code</span>
</summary>
<pre><code class="python">def discrete_marching_cubes(vtk_image_reader,
n_labels=1,
start_label=1,
end_label=1,
compute_normals_on=True,
return_polydata=True
):
"""
Compute dmc on segmentation image.
Creates a surface mesh (polydata) that closely covers binary (discrete) segmentations.
Parameters
----------
vtk_image_reader : vtk.Filter
VTK Filter pipeline to apply discrete marching cubes to.
n_labels : int, optional
Number of labes to create mesh for, by default 1
start_label : int, optional
Starting index of labels to mesh, by default 1
end_label : int, optional
Ending index of labels to mesh, by default 1
compute_normals_on : bool, optional
Calculate normals to surface, by default True
return_polydata : bool, optional
Whether to return a vtk.polydata or not (`vtk.Filter` pipeline instead), by default True
Returns
-------
vtk.Filter Pipeline
Returns a pipeline which more functions can be chained too - this improves performance.
OR
vtk.Polydata
Returns a polydata (surface mesh).
"""
dmc = vtk.vtkDiscreteMarchingCubes()
dmc.SetInputConnection(vtk_image_reader.GetOutputPort())
if compute_normals_on is True:
dmc.ComputeNormalsOn()
dmc.GenerateValues(n_labels, start_label, end_label)
dmc.Update()
if return_polydata is True:
return dmc.GetOutput()
elif return_polydata is False:
return dmc</code></pre>
</details>
</dd>
</dl>
</section>
<section>
</section>
</article>
<nav id="sidebar">
<h1>Index</h1>
<div class="toc">
<ul></ul>
</div>
<ul id="index">
<li><h3>Super-module</h3>
<ul>
<li><code><a title="pymskt.mesh" href="index.html">pymskt.mesh</a></code></li>
</ul>
</li>
<li><h3><a href="#header-functions">Functions</a></h3>
<ul class="">
<li><code><a title="pymskt.mesh.createMesh.continuous_marching_cubes" href="#pymskt.mesh.createMesh.continuous_marching_cubes">continuous_marching_cubes</a></code></li>
<li><code><a title="pymskt.mesh.createMesh.create_surface_mesh" href="#pymskt.mesh.createMesh.create_surface_mesh">create_surface_mesh</a></code></li>
<li><code><a title="pymskt.mesh.createMesh.discrete_marching_cubes" href="#pymskt.mesh.createMesh.discrete_marching_cubes">discrete_marching_cubes</a></code></li>
</ul>
</li>
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