diff --git a/Python_html/04_Image_Display.html b/Python_html/04_Image_Display.html index 0020d96c..28706237 100644 --- a/Python_html/04_Image_Display.html +++ b/Python_html/04_Image_Display.html @@ -7550,7 +7550,7 @@

Image Display No description has been provided for this image

The native SimpleITK approach to displaying images is to use an external viewing program. In the notebook environment it is convenient to use matplotlib to display inline images and if the need arises we can implement some reasonably rich inline graphical user interfaces, combining control components from the ipywidgets package and matplotlib based display.

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In this notebook we cover the usage of external programs and matplotlib for viewing images. We also instantiate a more involved inline interface that uses ipywidgets to control display. For the latter type of moderately complex display, used in many of the notebooks, take a look at the gui.py file.

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In this notebook we cover the usage of external programs and matplotlib for viewing images. We also instantiate a more involved inline interface that uses ipywidgets to control display. For the latter type of moderately complex display, used in many of the notebooks, take a look at the gui.py file.

diff --git a/Python_html/05_Results_Visualization.html b/Python_html/05_Results_Visualization.html index 76a137fb..641f45f7 100644 --- a/Python_html/05_Results_Visualization.html +++ b/Python_html/05_Results_Visualization.html @@ -7558,7 +7558,7 @@

Visualization of Segmentation and Registration Results CastImageFilter -
  • ResampleImageFilter, one of the more important filters in your toolbox, see this notebook for additional usage details.
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  • ResampleImageFilter, one of the more important filters in your toolbox, see this notebook for additional usage details.
  • TileImageFilter
  • CheckerBoardImageFilter
  • ComposeImageFilter
    • diff --git a/Python_html/21_Transforms_and_Resampling.html b/Python_html/21_Transforms_and_Resampling.html index 30d4a2a5..471bcc67 100644 --- a/Python_html/21_Transforms_and_Resampling.html +++ b/Python_html/21_Transforms_and_Resampling.html @@ -7773,7 +7773,7 @@

    Creating and Manipulating Transfor
    diff --git a/Python_html/34_Segmentation_Evaluation.html b/Python_html/34_Segmentation_Evaluation.html index 50a7d38f..09307fde 100644 --- a/Python_html/34_Segmentation_Evaluation.html +++ b/Python_html/34_Segmentation_Evaluation.html @@ -7571,7 +7571,7 @@

    Segmentation Evaluation -

    Note: The approach described here can also be used to evaluate Registration, as illustrated in the free form deformation notebook.

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    Note: The approach described here can also be used to evaluate Registration, as illustrated in the free form deformation notebook.

    Recommended read: A community effort describing limitations of various evaluation metrics, A. Reinke et al., "Common Limitations of Image Processing Metrics: A Picture Story", available from arxiv (PDF).

    diff --git a/Python_html/61_Registration_Introduction_Continued.html b/Python_html/61_Registration_Introduction_Continued.html index 25451d5e..27f741af 100644 --- a/Python_html/61_Registration_Introduction_Continued.html +++ b/Python_html/61_Registration_Introduction_Continued.html @@ -7549,7 +7549,7 @@
    diff --git a/Python_html/65_Registration_FFD.html b/Python_html/65_Registration_FFD.html index ab05211f..4a5f5f18 100644 --- a/Python_html/65_Registration_FFD.html +++ b/Python_html/65_Registration_FFD.html @@ -8034,7 +8034,7 @@

    Perform Registration

    diff --git a/Python_html/70_Data_Augmentation.html b/Python_html/70_Data_Augmentation.html index 6be7250e..645f4453 100644 --- a/Python_html/70_Data_Augmentation.html +++ b/Python_html/70_Data_Augmentation.html @@ -8571,7 +8571,7 @@

    Radial Distortion
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    Transferring deformations - exercise for the interested reader

    Using SimpleITK we can readily transfer deformations from a spatio-temporal data set to another spatial data set to simulate temporal behavior. Case in point, using a 4D (3D+time) CT of the thorax we can estimate the respiratory motion using non-rigid registration and Free Form Deformation or displacement field transformations. We can then register a new spatial data set to the original spatial CT (non-rigidly) followed by application of the temporal deformations.

    +

    Transferring deformations - exercise for the interested reader

    Using SimpleITK we can readily transfer deformations from a spatio-temporal data set to another spatial data set to simulate temporal behavior. Case in point, using a 4D (3D+time) CT of the thorax we can estimate the respiratory motion using non-rigid registration and Free Form Deformation or displacement field transformations. We can then register a new spatial data set to the original spatial CT (non-rigidly) followed by application of the temporal deformations.

    Note that unlike the arbitrary spatial transformations we used for data-augmentation above this approach is more computationally expensive as it involves multiple non-rigid registrations. Also note that as the goal is to use the estimated transformations to create plausible deformations you may be able to relax the required registration accuracy.

    diff --git a/Python_html/71_Trust_But_Verify.html b/Python_html/71_Trust_But_Verify.html index 20a2e6ed..f6768a76 100644 --- a/Python_html/71_Trust_But_Verify.html +++ b/Python_html/71_Trust_But_Verify.html @@ -7548,7 +7548,7 @@