3_Segmentation.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {
    "slideshow": {
     "slide_type": "slide"
    }
   },
   "source": [
    "# Image Segmentation"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "slideshow": {
     "slide_type": "slide"
    }
   },
   "source": [
    "## Overview"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "slideshow": {
     "slide_type": "subslide"
    }
   },
   "source": [
    "### Learning Objectives\n",
    "\n",
    "* Gain familiarity with the different categories of segmentation methods available in ITK\n",
    "* Understand the difference between region growing methods and how their parameters change their behavior\n",
    "* Understand the influence of terms in level set segmentation methods"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "slideshow": {
     "slide_type": "slide"
    }
   },
   "source": [
    "### ITK Segmentation Method Categories\n",
    "\n",
    "![Segmentation categories](data/segmentation-categories.png)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "slideshow": {
     "slide_type": "slide"
    }
   },
   "source": [
    "### Region Growing Methods\n",
    "\n",
    "The basic approach of a *region growing algorithm* is to **start from a seed region (typically one or more\n",
    "pixels)** that are considered to be inside the object to be segmented. The **pixels neighboring this\n",
    "region are evaluated to determine if they should also be considered part of the object**. If so, **they are\n",
    "added to the region and the process continues as long as new pixels are added to the region**. Region\n",
    "growing algorithms vary depending on the criteria used to decide whether a pixel should be included\n",
    "in the region or not, the type connectivity used to determine neighbors, and the strategy used to visit\n",
    "neighboring pixels."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "slideshow": {
     "slide_type": "fragment"
    }
   },
   "source": [
    "Example region growing methods available in ITK:\n",
    "\n",
    "- ConfidenceConnected\n",
    "- ConnectedThreshold\n",
    "- IsolatedConnected"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "slideshow": {
     "slide_type": "subslide"
    }
   },
   "source": [
    "#### Confidence Connected\n",
    "\n",
    "![Confidence Connected](data/confidence-connected.png)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "slideshow": {
     "slide_type": "subslide"
    }
   },
   "source": [
    "#### Connected Threshold\n",
    "\n",
    "![Connected Threshold](data/connected-threshold.png)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "slideshow": {
     "slide_type": "subslide"
    }
   },
   "source": [
    "#### Isolated Connected\n",
    "\n",
    "![Isolated Connected](data/isolated-connected.png)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "slideshow": {
     "slide_type": "slide"
    }
   },
   "source": [
    "### Level Set Segmentation Methods"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "slideshow": {
     "slide_type": "subslide"
    }
   },
   "source": [
    "### Level Set Concept\n",
    "\n",
    "![Level set concept](data/level-set-concept.png)\n",
    "\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "slideshow": {
     "slide_type": "subslide"
    }
   },
   "source": [
    "#### Level Set Evolution\n",
    "\n",
    "![Level Set evolution](data/level-set-evolution.png)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "slideshow": {
     "slide_type": "subslide"
    }
   },
   "source": [
    "#### Level Set Function\n",
    "\n",
    "$\\phi_{t} = - \\alpha  \\mathbf{A}(\\mathbf{x})\\cdot\\nabla\\phi - \\beta   P(\\mathbf{x})\\mid\\nabla\\phi\\mid + \\gamma Z(\\mathbf{x})\\kappa\\mid\\nabla\\phi\\mid$\n",
    "\n",
    "- $\\mathbf{A}(\\mathbf{x})$: **Advection**\n",
    "- $P(\\mathbf{x})$: **Propagation**\n",
    "- $Z(\\mathbf{x})$: **Spatial modifier for the mean curvature $\\kappa$**\n",
    "- $\\alpha, \\beta, \\gamma$ are scalar constants"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "slideshow": {
     "slide_type": "fragment"
    }
   },
   "source": [
    "- Track the evolution of contours and surfaces by computing the update to the solution $\\phi$ of the PDE\n",
    "- Contour (initial solution) is initialized by the user\n",
    "- Evolve $\\phi$ until\n",
    "  - It fits the form of the segment you are trying to fit (shape detection)\n",
    "  - It segments the features you want (segmentation)\n",
    "- Evolution is uni-directional (inwards or outwards)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "slideshow": {
     "slide_type": "subslide"
    }
   },
   "source": [
    "For more information, see:\n",
    "\n",
    "- The [*Segmentation* chapter](https://itk.org/ITKSoftwareGuide/html/Book2/ITKSoftwareGuide-Book2ch4.html#x37-1770004) of Book 2 of the ITK Software Guide, including the sections on [*Region Growing*](https://itk.org/ITKSoftwareGuide/html/Book2/ITKSoftwareGuide-Book2ch4.html#x37-1780004.1) and [*Level Set Segmentation*](https://itk.org/ITKSoftwareGuide/html/Book2/ITKSoftwareGuide-Book2ch4.html#x37-1890004.3)."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "slideshow": {
     "slide_type": "slide"
    }
   },
   "source": [
    "## Tutorial"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 197,
   "metadata": {
    "slideshow": {
     "slide_type": "subslide"
    }
   },
   "outputs": [],
   "source": [
    "import numpy as np\n",
    "import itk\n",
    "from itkwidgets import view\n",
    "from matplotlib import pyplot as plt\n",
    "%matplotlib inline\n",
    "from ipywidgets import interact"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "slideshow": {
     "slide_type": "subslide"
    }
   },
   "source": [
    "#### itk.ConfidenceConnectedImageFilter\n",
    "\n",
    "From [itk::ConfidenceConnectedImageFilter documentation](https://itk.org/Doxygen/html/classitk_1_1ConfidenceConnectedImageFilter.html):\n",
    "\n",
    "> Segment pixels with similar statistics using connectivity.\n",
    "\n",
    "> This filter extracts a connected set of pixels whose pixel intensities are consistent with the pixel statistics of a seed point. The mean and variance across a neighborhood (8-connected, 26-connected, etc.) are calculated for a seed point. Then pixels connected to this seed point whose values are within the confidence interval for the seed point are grouped. The width of the confidence interval is controlled by the \"Multiplier\" variable (the confidence interval is the mean plus or minus the \"Multiplier\" times the standard deviation). If the intensity variations across a segment were gaussian, a \"Multiplier\" setting of 2.5 would define a confidence interval wide enough to capture 99% of samples in the segment.\n",
    "\n",
    "> After this initial segmentation is calculated, the mean and variance are re-calculated. All the pixels in the previous segmentation are used to calculate the mean the standard deviation (as opposed to using the pixels in the neighborhood of the seed point). The segmentation is then recalculated using these refined estimates for the mean and variance of the pixel values. This process is repeated for the specified number of iterations. Setting the \"NumberOfIterations\" to zero stops the algorithm after the initial segmentation from the seed point."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 71,
   "metadata": {
    "slideshow": {
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   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Help on itkConfidenceConnectedImageFilterIUC2ISS2 in module itkConfidenceConnectedImageFilterPython object:\n",
      "\n",
      "class itkConfidenceConnectedImageFilterIUC2ISS2(itkImageToImageFilterAPython.itkImageToImageFilterIUC2ISS2)\n",
      " |  Segment pixels with similar statistics using connectivity.\n",
      " |  \n",
      " |  This filter extracts a connected set of pixels whose pixel intensities\n",
      " |  are consistent with the pixel statistics of a seed point. The mean and\n",
      " |  variance across a neighborhood (8-connected, 26-connected, etc.) are\n",
      " |  calculated for a seed point. Then pixels connected to this seed point\n",
      " |  whose values are within the confidence interval for the seed point are\n",
      " |  grouped. The width of the confidence interval is controlled by the\n",
      " |  \"Multiplier\" variable (the confidence interval is the mean plus or\n",
      " |  minus the \"Multiplier\" times the standard deviation). If the\n",
      " |  intensity variations across a segment were gaussian, a \"Multiplier\"\n",
      " |  setting of 2.5 would define a confidence interval wide enough to\n",
      " |  capture 99% of samples in the segment.\n",
      " |  \n",
      " |  After this initial segmentation is calculated, the mean and variance\n",
      " |  are re-calculated. All the pixels in the previous segmentation are\n",
      " |  used to calculate the mean the standard deviation (as opposed to using\n",
      " |  the pixels in the neighborhood of the seed point). The segmentation is\n",
      " |  then recalculated using these refined estimates for the mean and\n",
      " |  variance of the pixel values. This process is repeated for the\n",
      " |  specified number of iterations. Setting the \"NumberOfIterations\" to\n",
      " |  zero stops the algorithm after the initial segmentation from the seed\n",
      " |  point.\n",
      " |  \n",
      " |  NOTE: the lower and upper threshold are restricted to lie within the\n",
      " |  valid numeric limits of the input data pixel type. Also, the limits\n",
      " |  may be adjusted to contain the seed point's intensity.\n",
      " |  \n",
      " |  Wiki Examples:\n",
      " |  \n",
      " |  All Examples\n",
      " |  \n",
      " |  C++ includes: itkConfidenceConnectedImageFilter.h\n",
      " |  \n",
      " |  Method resolution order:\n",
      " |      itkConfidenceConnectedImageFilterIUC2ISS2\n",
      " |      itkImageToImageFilterAPython.itkImageToImageFilterIUC2ISS2\n",
      " |      itkImageSourcePython.itkImageSourceISS2\n",
      " |      ITKCommonBasePython.itkProcessObject\n",
      " |      ITKCommonBasePython.itkObject\n",
      " |      ITKCommonBasePython.itkLightObject\n",
      " |      builtins.object\n",
      " |  \n",
      " |  Methods defined here:\n",
      " |  \n",
      " |  AddSeed = itkConfidenceConnectedImageFilterIUC2ISS2_AddSeed(...)\n",
      " |      itkConfidenceConnectedImageFilterIUC2ISS2_AddSeed(itkConfidenceConnectedImageFilterIUC2ISS2 self, itkIndex2 seed)\n",
      " |      \n",
      " |      Add seed point.\n",
      " |  \n",
      " |  ClearSeeds = itkConfidenceConnectedImageFilterIUC2ISS2_ClearSeeds(...)\n",
      " |      itkConfidenceConnectedImageFilterIUC2ISS2_ClearSeeds(itkConfidenceConnectedImageFilterIUC2ISS2 self)\n",
      " |      \n",
      " |      Clear all the seeds.\n",
      " |  \n",
      " |  Clone = itkConfidenceConnectedImageFilterIUC2ISS2_Clone(...)\n",
      " |      itkConfidenceConnectedImageFilterIUC2ISS2_Clone(itkConfidenceConnectedImageFilterIUC2ISS2 self) -> itkConfidenceConnectedImageFilterIUC2ISS2_Pointer\n",
      " |  \n",
      " |  GetInitialNeighborhoodRadius = itkConfidenceConnectedImageFilterIUC2ISS2_GetInitialNeighborhoodRadius(...)\n",
      " |      itkConfidenceConnectedImageFilterIUC2ISS2_GetInitialNeighborhoodRadius(itkConfidenceConnectedImageFilterIUC2ISS2 self) -> unsigned int const &\n",
      " |  \n",
      " |  GetMean = itkConfidenceConnectedImageFilterIUC2ISS2_GetMean(...)\n",
      " |      itkConfidenceConnectedImageFilterIUC2ISS2_GetMean(itkConfidenceConnectedImageFilterIUC2ISS2 self) -> double const &\n",
      " |      \n",
      " |      Method to get access to the\n",
      " |      mean of the pixels accepted in the output region. This method should\n",
      " |      only be invoked after the filter has been executed using the Update()\n",
      " |      method.\n",
      " |  \n",
      " |  GetMultiplier = itkConfidenceConnectedImageFilterIUC2ISS2_GetMultiplier(...)\n",
      " |      itkConfidenceConnectedImageFilterIUC2ISS2_GetMultiplier(itkConfidenceConnectedImageFilterIUC2ISS2 self) -> double\n",
      " |  \n",
      " |  GetNumberOfIterations = itkConfidenceConnectedImageFilterIUC2ISS2_GetNumberOfIterations(...)\n",
      " |      itkConfidenceConnectedImageFilterIUC2ISS2_GetNumberOfIterations(itkConfidenceConnectedImageFilterIUC2ISS2 self) -> unsigned int\n",
      " |  \n",
      " |  GetPointer = itkConfidenceConnectedImageFilterIUC2ISS2_GetPointer(...)\n",
      " |      itkConfidenceConnectedImageFilterIUC2ISS2_GetPointer(itkConfidenceConnectedImageFilterIUC2ISS2 self) -> itkConfidenceConnectedImageFilterIUC2ISS2\n",
      " |  \n",
      " |  GetReplaceValue = itkConfidenceConnectedImageFilterIUC2ISS2_GetReplaceValue(...)\n",
      " |      itkConfidenceConnectedImageFilterIUC2ISS2_GetReplaceValue(itkConfidenceConnectedImageFilterIUC2ISS2 self) -> short\n",
      " |  \n",
      " |  GetSeeds = itkConfidenceConnectedImageFilterIUC2ISS2_GetSeeds(...)\n",
      " |      itkConfidenceConnectedImageFilterIUC2ISS2_GetSeeds(itkConfidenceConnectedImageFilterIUC2ISS2 self) -> std::vector< itkIndex2,std::allocator< itkIndex2 > > const &\n",
      " |      \n",
      " |      Method to access seed\n",
      " |      container\n",
      " |  \n",
      " |  GetVariance = itkConfidenceConnectedImageFilterIUC2ISS2_GetVariance(...)\n",
      " |      itkConfidenceConnectedImageFilterIUC2ISS2_GetVariance(itkConfidenceConnectedImageFilterIUC2ISS2 self) -> double const &\n",
      " |      \n",
      " |      Method to get access to\n",
      " |      the variance of the pixels accepted in the output region. This method\n",
      " |      should only be invoked after the filter has been executed using the\n",
      " |      Update() method.\n",
      " |  \n",
      " |  PrintSelf = itkConfidenceConnectedImageFilterIUC2ISS2_PrintSelf(...)\n",
      " |      itkConfidenceConnectedImageFilterIUC2ISS2_PrintSelf(itkConfidenceConnectedImageFilterIUC2ISS2 self, ostream os, itkIndent indent)\n",
      " |  \n",
      " |  SetInitialNeighborhoodRadius = itkConfidenceConnectedImageFilterIUC2ISS2_SetInitialNeighborhoodRadius(...)\n",
      " |      itkConfidenceConnectedImageFilterIUC2ISS2_SetInitialNeighborhoodRadius(itkConfidenceConnectedImageFilterIUC2ISS2 self, unsigned int const _arg)\n",
      " |      \n",
      " |      Get/Set the radius of the neighborhood over which the statistics are\n",
      " |      evaluated\n",
      " |  \n",
      " |  SetMultiplier = itkConfidenceConnectedImageFilterIUC2ISS2_SetMultiplier(...)\n",
      " |      itkConfidenceConnectedImageFilterIUC2ISS2_SetMultiplier(itkConfidenceConnectedImageFilterIUC2ISS2 self, double const _arg)\n",
      " |      \n",
      " |      Set/Get the\n",
      " |      multiplier to define the confidence interval. Multiplier can be\n",
      " |      anything greater than zero. A typical value is 2.5\n",
      " |  \n",
      " |  SetNumberOfIterations = itkConfidenceConnectedImageFilterIUC2ISS2_SetNumberOfIterations(...)\n",
      " |      itkConfidenceConnectedImageFilterIUC2ISS2_SetNumberOfIterations(itkConfidenceConnectedImageFilterIUC2ISS2 self, unsigned int const _arg)\n",
      " |      \n",
      " |      Set/Get the\n",
      " |      number of iterations\n",
      " |  \n",
      " |  SetReplaceValue = itkConfidenceConnectedImageFilterIUC2ISS2_SetReplaceValue(...)\n",
      " |      itkConfidenceConnectedImageFilterIUC2ISS2_SetReplaceValue(itkConfidenceConnectedImageFilterIUC2ISS2 self, short const _arg)\n",
      " |      \n",
      " |      Set/Get value to\n",
      " |      replace thresholded pixels\n",
      " |  \n",
      " |  SetSeed = itkConfidenceConnectedImageFilterIUC2ISS2_SetSeed(...)\n",
      " |      itkConfidenceConnectedImageFilterIUC2ISS2_SetSeed(itkConfidenceConnectedImageFilterIUC2ISS2 self, itkIndex2 seed)\n",
      " |      \n",
      " |      Set seed point. This method\n",
      " |      is deprecated, please use AddSeed()\n",
      " |  \n",
      " |  __init__(self, *args, **kwargs)\n",
      " |      Initialize self.  See help(type(self)) for accurate signature.\n",
      " |  \n",
      " |  __repr__ = _swig_repr(self)\n",
      " |  \n",
      " |  __swig_destroy__ = delete_itkConfidenceConnectedImageFilterIUC2ISS2(...)\n",
      " |      delete_itkConfidenceConnectedImageFilterIUC2ISS2(itkConfidenceConnectedImageFilterIUC2ISS2 self)\n",
      " |  \n",
      " |  ----------------------------------------------------------------------\n",
      " |  Static methods defined here:\n",
      " |  \n",
      " |  New(*args, **kargs)\n",
      " |      New() -> itkConfidenceConnectedImageFilterIUC2ISS2\n",
      " |      \n",
      " |      Create a new object of the class itkConfidenceConnectedImageFilterIUC2ISS2 and set the input and the parameters if some\n",
      " |      named or non-named arguments are passed to that method.\n",
      " |      \n",
      " |      New() tries to assign all the non named parameters to the input of the new objects - the\n",
      " |      first non named parameter in the first input, etc.\n",
      " |      \n",
      " |      The named parameters are used by calling the method with the same name prefixed by 'Set'.\n",
      " |      \n",
      " |      Ex:\n",
      " |      \n",
      " |        itkConfidenceConnectedImageFilterIUC2ISS2.New( reader, Threshold=10 )\n",
      " |      \n",
      " |      is (most of the time) equivalent to:\n",
      " |      \n",
      " |        obj = itkConfidenceConnectedImageFilterIUC2ISS2.New()\n",
      " |        obj.SetInput( 0, reader.GetOutput() )\n",
      " |        obj.SetThreshold( 10 )\n",
      " |  \n",
      " |  __New_orig__() -> 'itkConfidenceConnectedImageFilterIUC2ISS2_Pointer'\n",
      " |      __New_orig__() -> itkConfidenceConnectedImageFilterIUC2ISS2_Pointer\n",
      " |  \n",
      " |  cast(obj:'itkLightObject') -> 'itkConfidenceConnectedImageFilterIUC2ISS2 *'\n",
      " |      cast(itkLightObject obj) -> itkConfidenceConnectedImageFilterIUC2ISS2\n",
      " |  \n",
      " |  ----------------------------------------------------------------------\n",
      " |  Data descriptors defined here:\n",
      " |  \n",
      " |  thisown\n",
      " |      The membership flag\n",
      " |  \n",
      " |  ----------------------------------------------------------------------\n",
      " |  Data and other attributes defined here:\n",
      " |  \n",
      " |  InputHasNumericTraitsCheck = 1\n",
      " |  \n",
      " |  OutputHasNumericTraitsCheck = 1\n",
      " |  \n",
      " |  ----------------------------------------------------------------------\n",
      " |  Methods inherited from itkImageToImageFilterAPython.itkImageToImageFilterIUC2ISS2:\n",
      " |  \n",
      " |  GetCoordinateTolerance = itkImageToImageFilterIUC2ISS2_GetCoordinateTolerance(...)\n",
      " |      itkImageToImageFilterIUC2ISS2_GetCoordinateTolerance(itkImageToImageFilterIUC2ISS2 self) -> double\n",
      " |  \n",
      " |  GetDirectionTolerance = itkImageToImageFilterIUC2ISS2_GetDirectionTolerance(...)\n",
      " |      itkImageToImageFilterIUC2ISS2_GetDirectionTolerance(itkImageToImageFilterIUC2ISS2 self) -> double\n",
      " |  \n",
      " |  GetInput = itkImageToImageFilterIUC2ISS2_GetInput(...)\n",
      " |      GetInput() -> itkImageUC2\n",
      " |      itkImageToImageFilterIUC2ISS2_GetInput(itkImageToImageFilterIUC2ISS2 self, unsigned int idx) -> itkImageUC2\n",
      " |  \n",
      " |  PopBackInput = itkImageToImageFilterIUC2ISS2_PopBackInput(...)\n",
      " |      itkImageToImageFilterIUC2ISS2_PopBackInput(itkImageToImageFilterIUC2ISS2 self)\n",
      " |  \n",
      " |  PopFrontInput = itkImageToImageFilterIUC2ISS2_PopFrontInput(...)\n",
      " |      itkImageToImageFilterIUC2ISS2_PopFrontInput(itkImageToImageFilterIUC2ISS2 self)\n",
      " |  \n",
      " |  PushBackInput = itkImageToImageFilterIUC2ISS2_PushBackInput(...)\n",
      " |      itkImageToImageFilterIUC2ISS2_PushBackInput(itkImageToImageFilterIUC2ISS2 self, itkImageUC2 image)\n",
      " |      \n",
      " |      Push/Pop the input of\n",
      " |      this process object. These methods allow a filter to model its input\n",
      " |      vector as a queue or stack. These routines may not be appropriate for\n",
      " |      all filters, especially filters with different types of inputs. These\n",
      " |      routines follow the semantics of STL.\n",
      " |      \n",
      " |      The routines are useful for applications that need to process\n",
      " |      \"rolling\" sets of images. For instance, if an application has 10\n",
      " |      images and they need to run a filter on images 1, 2, 3, 4, then run\n",
      " |      the filter on images 2, 3, 4, 5, then run the filter on images 3, 4,\n",
      " |      5, 6, the application can accomplish this by popping an input off the\n",
      " |      front of the input list and push a new image onto the back of input\n",
      " |      list. Again, this only makes sense for filters that single type of\n",
      " |      input.\n",
      " |      \n",
      " |      Other uses are also possible. For a single input filter, pushing and\n",
      " |      popping inputs allow the application to temporarily replace an input\n",
      " |      to a filter.\n",
      " |  \n",
      " |  PushFrontInput = itkImageToImageFilterIUC2ISS2_PushFrontInput(...)\n",
      " |      itkImageToImageFilterIUC2ISS2_PushFrontInput(itkImageToImageFilterIUC2ISS2 self, itkImageUC2 image)\n",
      " |  \n",
      " |  SetCoordinateTolerance = itkImageToImageFilterIUC2ISS2_SetCoordinateTolerance(...)\n",
      " |      itkImageToImageFilterIUC2ISS2_SetCoordinateTolerance(itkImageToImageFilterIUC2ISS2 self, double const _arg)\n",
      " |      \n",
      " |      get/set the\n",
      " |      Coordinate tolerance This tolerance is used when comparing the space\n",
      " |      defined by the input images. With ITK 4.x a requirement has been added\n",
      " |      that both input images be congruent in space.\n",
      " |  \n",
      " |  SetDirectionTolerance = itkImageToImageFilterIUC2ISS2_SetDirectionTolerance(...)\n",
      " |      itkImageToImageFilterIUC2ISS2_SetDirectionTolerance(itkImageToImageFilterIUC2ISS2 self, double const _arg)\n",
      " |      \n",
      " |      get/set the\n",
      " |      direction tolerance This tolerance is used to make sure that all input\n",
      " |      images are oriented the same before performing the filter's\n",
      " |      transformations.\n",
      " |  \n",
      " |  SetInput = itkImageToImageFilterIUC2ISS2_SetInput(...)\n",
      " |      SetInput(itkImageUC2 image)\n",
      " |      itkImageToImageFilterIUC2ISS2_SetInput(itkImageToImageFilterIUC2ISS2 self, unsigned int arg0, itkImageUC2 image)\n",
      " |  \n",
      " |  ----------------------------------------------------------------------\n",
      " |  Methods inherited from itkImageSourcePython.itkImageSourceISS2:\n",
      " |  \n",
      " |  GetOutput = itkImageSourceISS2_GetOutput(...)\n",
      " |      GetOutput() -> itkImageSS2\n",
      " |      GetOutput() -> itkImageSS2\n",
      " |      itkImageSourceISS2_GetOutput(itkImageSourceISS2 self, unsigned int idx) -> itkImageSS2\n",
      " |      \n",
      " |      Get the output data of\n",
      " |      this process object. The output of this function is not valid until an\n",
      " |      appropriate Update() method has been called, either explicitly or\n",
      " |      implicitly. Both the filter itself and the data object have Update()\n",
      " |      methods, and both methods update the data. Here are three ways to use\n",
      " |      GetOutput() and make sure the data is valid. In these particular\n",
      " |      ProcessObjects involved are filters. The same\n",
      " |      \n",
      " |      In this situation, someFilter and anotherFilter are said to constitute\n",
      " |      a pipeline.\n",
      " |      \n",
      " |      either order.)\n",
      " |      \n",
      " |      Note that Update() is not called automatically except within a\n",
      " |      StreamingImageFilter) is activated, it may be more efficient to use a\n",
      " |      pipeline than to call Update() once for each filter in turn.\n",
      " |      \n",
      " |      For an image, the data generated is for the requested Region, which\n",
      " |      can be set using ImageBase::SetRequestedRegion(). By default, the\n",
      " |      largest possible region is requested.\n",
      " |      \n",
      " |      For Filters which have multiple outputs of different types, the\n",
      " |      GetOutput() method assumes the output is of OutputImageType. For the\n",
      " |      GetOutput(unsigned int) method, a dynamic_cast is performed incase the\n",
      " |      filter has outputs of different types or image types. Derived classes\n",
      " |      should have names get methods for these outputs.\n",
      " |  \n",
      " |  GraftNthOutput = itkImageSourceISS2_GraftNthOutput(...)\n",
      " |      itkImageSourceISS2_GraftNthOutput(itkImageSourceISS2 self, unsigned int idx, itkDataObject output)\n",
      " |      \n",
      " |      Graft the specified\n",
      " |      data object onto this ProcessObject's idx'th output. This is similar\n",
      " |      to the GraftOutput method except it allows you to specify which output\n",
      " |      is affected. The specified index must be a valid output number (less\n",
      " |      than ProcessObject::GetNumberOfIndexedOutputs()). See the GraftOutput\n",
      " |      for general usage information.\n",
      " |  \n",
      " |  GraftOutput = itkImageSourceISS2_GraftOutput(...)\n",
      " |      GraftOutput(itkDataObject output)\n",
      " |      itkImageSourceISS2_GraftOutput(itkImageSourceISS2 self, std::string const & key, itkDataObject output)\n",
      " |      \n",
      " |      Graft the specified\n",
      " |      data object onto this ProcessObject's named output. This is similar to\n",
      " |      the GraftOutput method except it allows you to specify which output is\n",
      " |      affected. See the GraftOutput for general usage information.\n",
      " |  \n",
      " |  MakeOutput = itkImageSourceISS2_MakeOutput(...)\n",
      " |      MakeOutput(unsigned long idx) -> itkDataObject_Pointer\n",
      " |      itkImageSourceISS2_MakeOutput(itkImageSourceISS2 self, std::string const & arg0) -> itkDataObject_Pointer\n",
      " |      \n",
      " |      Make a DataObject of the\n",
      " |      correct type to used as the specified output. Every ProcessObject\n",
      " |      subclass must be able to create a DataObject that can be used as a\n",
      " |      specified output. This method is automatically called when\n",
      " |      DataObject::DisconnectPipeline() is called.\n",
      " |      DataObject::DisconnectPipeline, disconnects a data object from being\n",
      " |      an output of its current source. When the data object is disconnected,\n",
      " |      the ProcessObject needs to construct a replacement output data object\n",
      " |      so that the ProcessObject is in a valid state. So\n",
      " |      DataObject::DisconnectPipeline eventually calls\n",
      " |      ProcessObject::MakeOutput. Note that MakeOutput always returns a\n",
      " |      SmartPointer to a DataObject. If a subclass of ImageSource has\n",
      " |      multiple outputs of different types, then that class must provide an\n",
      " |      implementation of MakeOutput().\n",
      " |  \n",
      " |  ----------------------------------------------------------------------\n",
      " |  Methods inherited from ITKCommonBasePython.itkProcessObject:\n",
      " |  \n",
      " |  AbortGenerateDataOff = itkProcessObject_AbortGenerateDataOff(...)\n",
      " |      itkProcessObject_AbortGenerateDataOff(itkProcessObject self)\n",
      " |  \n",
      " |  AbortGenerateDataOn = itkProcessObject_AbortGenerateDataOn(...)\n",
      " |      itkProcessObject_AbortGenerateDataOn(itkProcessObject self)\n",
      " |      \n",
      " |      Turn on and off\n",
      " |      the AbortGenerateData flag.\n",
      " |  \n",
      " |  EnlargeOutputRequestedRegion = itkProcessObject_EnlargeOutputRequestedRegion(...)\n",
      " |      itkProcessObject_EnlargeOutputRequestedRegion(itkProcessObject self, itkDataObject arg0)\n",
      " |      \n",
      " |      Give\n",
      " |      the process object a chance to indictate that it will produce more\n",
      " |      filters must compute the entire output at once or can only produce\n",
      " |      output in complete slices. Such filters cannot handle smaller\n",
      " |      requested regions. These filters must provide an implementation of\n",
      " |      this method, setting the output requested region to the size they will\n",
      " |      produce. By default, a process object does not modify the size of the\n",
      " |      output requested region.\n",
      " |  \n",
      " |  GetAbortGenerateData = itkProcessObject_GetAbortGenerateData(...)\n",
      " |      itkProcessObject_GetAbortGenerateData(itkProcessObject self) -> bool const &\n",
      " |      \n",
      " |      Get the\n",
      " |      AbortGenerateData flag for the process object.\n",
      " |  \n",
      " |  GetIndexedInputs = itkProcessObject_GetIndexedInputs(...)\n",
      " |      itkProcessObject_GetIndexedInputs(itkProcessObject self) -> vectoritkDataObject\n",
      " |      \n",
      " |      Return an array\n",
      " |      with all the indexed inputs.\n",
      " |  \n",
      " |  GetIndexedOutputs = itkProcessObject_GetIndexedOutputs(...)\n",
      " |      itkProcessObject_GetIndexedOutputs(itkProcessObject self) -> vectoritkDataObject\n",
      " |      \n",
      " |      Return an array\n",
      " |      with the indexed outputs.\n",
      " |  \n",
      " |  GetInputNames = itkProcessObject_GetInputNames(...)\n",
      " |      itkProcessObject_GetInputNames(itkProcessObject self) -> vectorstring\n",
      " |      \n",
      " |      Return an array with\n",
      " |      the names of the inputs defined.\n",
      " |      \n",
      " |      The names are ordered lexicographically, and match the order of the\n",
      " |      data object produced by GetInputs().\n",
      " |  \n",
      " |  GetInputs = itkProcessObject_GetInputs(...)\n",
      " |      itkProcessObject_GetInputs(itkProcessObject self) -> vectoritkDataObject\n",
      " |      \n",
      " |      Return an array of\n",
      " |      DataObjects with the defined named inputs.\n",
      " |      \n",
      " |      The order of the inputs matches the order of the input names produced\n",
      " |      by GetInputNames().\n",
      " |  \n",
      " |  GetMultiThreader = itkProcessObject_GetMultiThreader(...)\n",
      " |      itkProcessObject_GetMultiThreader(itkProcessObject self) -> itkMultiThreader\n",
      " |      \n",
      " |      Return the\n",
      " |      multithreader used by this class.\n",
      " |  \n",
      " |  GetNumberOfIndexedInputs = itkProcessObject_GetNumberOfIndexedInputs(...)\n",
      " |      itkProcessObject_GetNumberOfIndexedInputs(itkProcessObject self) -> unsigned long\n",
      " |      \n",
      " |      Get the\n",
      " |      number of defined Indexed inputs.\n",
      " |      \n",
      " |      This is merely the size of the index input vector, not the number of\n",
      " |      inputs that have valid DataObject's assigned. The Primary inputs is\n",
      " |      handled as a special case. Use GetNumberOfValidRequiredInputs() to\n",
      " |      determine how many indexed inputs are non-null.\n",
      " |      \n",
      " |      All indexed inputs up to this value are defined.\n",
      " |      \n",
      " |      See:   GetNumberOfInputs\n",
      " |      \n",
      " |      See:   GetNumberOfValidRequiredInputs\n",
      " |  \n",
      " |  GetNumberOfIndexedOutputs = itkProcessObject_GetNumberOfIndexedOutputs(...)\n",
      " |      itkProcessObject_GetNumberOfIndexedOutputs(itkProcessObject self) -> unsigned long\n",
      " |      \n",
      " |      The\n",
      " |      number of defined Indexed outputs.\n",
      " |  \n",
      " |  GetNumberOfInputs = itkProcessObject_GetNumberOfInputs(...)\n",
      " |      itkProcessObject_GetNumberOfInputs(itkProcessObject self) -> unsigned long\n",
      " |      \n",
      " |      Get the size of\n",
      " |      the input container.\n",
      " |      \n",
      " |      If the Primary input is ITK_NULLPTR it is not counted. This is not the\n",
      " |      number of inputs that have valid DataObject's assigned. Use\n",
      " |      GetNumberOfValidRequiredInputs() to determine how many inputs are non-\n",
      " |      null.\n",
      " |      \n",
      " |      See:   GetNumberOfValidRequiredInputs\n",
      " |      \n",
      " |      See:   GetNumberOfIndexedInputs\n",
      " |  \n",
      " |  GetNumberOfOutputs = itkProcessObject_GetNumberOfOutputs(...)\n",
      " |      itkProcessObject_GetNumberOfOutputs(itkProcessObject self) -> unsigned long\n",
      " |      \n",
      " |      Get the size of\n",
      " |      the output container.\n",
      " |  \n",
      " |  GetNumberOfThreads = itkProcessObject_GetNumberOfThreads(...)\n",
      " |      itkProcessObject_GetNumberOfThreads(itkProcessObject self) -> unsigned int const &\n",
      " |  \n",
      " |  GetNumberOfValidRequiredInputs = itkProcessObject_GetNumberOfValidRequiredInputs(...)\n",
      " |      itkProcessObject_GetNumberOfValidRequiredInputs(itkProcessObject self) -> unsigned long\n",
      " |      \n",
      " |      Get\n",
      " |      the number of valid indexed inputs.\n",
      " |      \n",
      " |      Returns the number of non-null indexed inputs. This method is used to\n",
      " |      determine whether the correct number of inputs are set to valid\n",
      " |      values.\n",
      " |      \n",
      " |      See:   GetNumberOfInputs\n",
      " |      \n",
      " |      See:   GetNumberOfIndexedInputs\n",
      " |  \n",
      " |  GetOutputNames = itkProcessObject_GetOutputNames(...)\n",
      " |      itkProcessObject_GetOutputNames(itkProcessObject self) -> vectorstring\n",
      " |      \n",
      " |      Return an array with\n",
      " |      the defined names of the outputs.\n",
      " |      \n",
      " |      The names are ordered lexicographically, and match the order of the\n",
      " |      data object produced by GetOutputs()\n",
      " |  \n",
      " |  GetOutputs = itkProcessObject_GetOutputs(...)\n",
      " |      itkProcessObject_GetOutputs(itkProcessObject self) -> vectoritkDataObject\n",
      " |      \n",
      " |      Return an array with the\n",
      " |      defined named outputs.\n",
      " |      \n",
      " |      The order of the outputs match the order of the input names produced\n",
      " |      by GetOutputNames().\n",
      " |  \n",
      " |  GetProgress = itkProcessObject_GetProgress(...)\n",
      " |      itkProcessObject_GetProgress(itkProcessObject self) -> float const &\n",
      " |      \n",
      " |      Get the execution\n",
      " |      progress of a process object.\n",
      " |      \n",
      " |      The progress is a floating number in [0,1] with 0 meaning no progress\n",
      " |      and 1 meaning the filter has completed execution.\n",
      " |  \n",
      " |  GetReleaseDataBeforeUpdateFlag = itkProcessObject_GetReleaseDataBeforeUpdateFlag(...)\n",
      " |      itkProcessObject_GetReleaseDataBeforeUpdateFlag(itkProcessObject self) -> bool const &\n",
      " |  \n",
      " |  GetReleaseDataFlag = itkProcessObject_GetReleaseDataFlag(...)\n",
      " |      itkProcessObject_GetReleaseDataFlag(itkProcessObject self) -> bool\n",
      " |  \n",
      " |  GetRequiredInputNames = itkProcessObject_GetRequiredInputNames(...)\n",
      " |      itkProcessObject_GetRequiredInputNames(itkProcessObject self) -> vectorstring\n",
      " |      \n",
      " |      Return an\n",
      " |      array with the names of the required inputs.\n",
      " |  \n",
      " |  HasInput = itkProcessObject_HasInput(...)\n",
      " |      itkProcessObject_HasInput(itkProcessObject self, std::string const & key) -> bool\n",
      " |      \n",
      " |      Return true if the input\n",
      " |      with this name is defined\n",
      " |  \n",
      " |  HasOutput = itkProcessObject_HasOutput(...)\n",
      " |      itkProcessObject_HasOutput(itkProcessObject self, std::string const & key) -> bool\n",
      " |      \n",
      " |      Return true if the output\n",
      " |      with this name is defined.\n",
      " |  \n",
      " |  PrepareOutputs = itkProcessObject_PrepareOutputs(...)\n",
      " |      itkProcessObject_PrepareOutputs(itkProcessObject self)\n",
      " |      \n",
      " |      An opportunity to\n",
      " |      deallocate a ProcessObject's bulk data storage. Some filters may wish\n",
      " |      to reuse existing bulk data storage to avoid unnecessary\n",
      " |      deallocation/allocation sequences. The default implementation calls\n",
      " |      Initialize() on each output. DataObject::Initialize() frees its bulk\n",
      " |      data by default.\n",
      " |  \n",
      " |  PropagateRequestedRegion = itkProcessObject_PropagateRequestedRegion(...)\n",
      " |      itkProcessObject_PropagateRequestedRegion(itkProcessObject self, itkDataObject output)\n",
      " |      \n",
      " |      Send the\n",
      " |      requested region information back up the pipeline (to the filters that\n",
      " |      precede this one).\n",
      " |  \n",
      " |  ReleaseDataBeforeUpdateFlagOff = itkProcessObject_ReleaseDataBeforeUpdateFlagOff(...)\n",
      " |      itkProcessObject_ReleaseDataBeforeUpdateFlagOff(itkProcessObject self)\n",
      " |  \n",
      " |  ReleaseDataBeforeUpdateFlagOn = itkProcessObject_ReleaseDataBeforeUpdateFlagOn(...)\n",
      " |      itkProcessObject_ReleaseDataBeforeUpdateFlagOn(itkProcessObject self)\n",
      " |  \n",
      " |  ReleaseDataFlagOff = itkProcessObject_ReleaseDataFlagOff(...)\n",
      " |      itkProcessObject_ReleaseDataFlagOff(itkProcessObject self)\n",
      " |  \n",
      " |  ReleaseDataFlagOn = itkProcessObject_ReleaseDataFlagOn(...)\n",
      " |      itkProcessObject_ReleaseDataFlagOn(itkProcessObject self)\n",
      " |  \n",
      " |  ResetPipeline = itkProcessObject_ResetPipeline(...)\n",
      " |      itkProcessObject_ResetPipeline(itkProcessObject self)\n",
      " |      \n",
      " |      Reset the pipeline.\n",
      " |      \n",
      " |      If an exception is thrown during an Update(), the pipeline may be in\n",
      " |      an inconsistent state. This method clears the internal state of the\n",
      " |      pipeline so Update() can be called.\n",
      " |  \n",
      " |  SetAbortGenerateData = itkProcessObject_SetAbortGenerateData(...)\n",
      " |      itkProcessObject_SetAbortGenerateData(itkProcessObject self, bool const _arg)\n",
      " |      \n",
      " |      Set the\n",
      " |      AbortGenerateData flag for the process object.\n",
      " |      \n",
      " |      Process objects may handle premature termination of execution in\n",
      " |      different ways.\n",
      " |  \n",
      " |  SetNumberOfThreads = itkProcessObject_SetNumberOfThreads(...)\n",
      " |      itkProcessObject_SetNumberOfThreads(itkProcessObject self, unsigned int _arg)\n",
      " |      \n",
      " |      Get/Set the\n",
      " |      number of threads to create when executing.\n",
      " |  \n",
      " |  SetProgress = itkProcessObject_SetProgress(...)\n",
      " |      itkProcessObject_SetProgress(itkProcessObject self, float progress)\n",
      " |      \n",
      " |      Deprecated Set the\n",
      " |      execution progress of a process object. The progress is a floating\n",
      " |      number in [0,1] with 0 meaning no progress and 1 meaning the filter\n",
      " |      has completed execution. The ProgressEvent is NOT invoked. This method\n",
      " |      is deprecated because filters should not be calling SetProgress\n",
      " |      directly but should be using UpdateProgress instead. We avoid the use\n",
      " |      of the itkSetClampMacro because that macro calls Modified on the\n",
      " |      filter, which will cause the filter to rerun even if it doesn't need\n",
      " |      to. Thus, we implement the SetClampMacro directly without the call to\n",
      " |      Modified.\n",
      " |  \n",
      " |  SetReleaseDataBeforeUpdateFlag = itkProcessObject_SetReleaseDataBeforeUpdateFlag(...)\n",
      " |      itkProcessObject_SetReleaseDataBeforeUpdateFlag(itkProcessObject self, bool const _arg)\n",
      " |      \n",
      " |      Turn\n",
      " |      on/off the flags to control whether the bulk data belonging to the\n",
      " |      outputs of this ProcessObject are released/reallocated during an\n",
      " |      Update(). In limited memory scenarios, a user may want to force the\n",
      " |      elements of a pipeline to release any bulk data that is going to be\n",
      " |      regenerated anyway during an Update() in order to control peak memory\n",
      " |      allocation. Note that this flag is different from the ReleaseDataFlag.\n",
      " |      ReleaseDataFlag manages the deallocation of a ProcessObject's bulk\n",
      " |      output data once that data has been consumed by a downstream\n",
      " |      ProcessObject. The ReleaseDataBeforeUpdateFlag manages the\n",
      " |      deallocation/reallocation of bulk data during a pipeline update to\n",
      " |      control peak memory utilization. Default value is on.\n",
      " |  \n",
      " |  SetReleaseDataFlag = itkProcessObject_SetReleaseDataFlag(...)\n",
      " |      itkProcessObject_SetReleaseDataFlag(itkProcessObject self, bool flag)\n",
      " |      \n",
      " |      Turn on/off the\n",
      " |      flags to control whether the bulk data belonging to the outputs of\n",
      " |      this ProcessObject are released after being used by a downstream\n",
      " |      ProcessObject. Default value is off. Another options for controlling\n",
      " |      memory utilization is the ReleaseDataBeforeUpdateFlag.\n",
      " |  \n",
      " |  Update = itkProcessObject_Update(...)\n",
      " |      itkProcessObject_Update(itkProcessObject self)\n",
      " |      \n",
      " |      Bring this filter up-to-\n",
      " |      date.\n",
      " |      \n",
      " |      Update() checks modified times against last execution times, and re-\n",
      " |      executes objects if necessary. A side effect of this method is that\n",
      " |      the whole pipeline may execute in order to bring this filter up-to-\n",
      " |      date. This method updates the currently prescribed requested region.\n",
      " |      If no requested region has been set on the output, then the requested\n",
      " |      region will be set to the largest possible region. Once the requested\n",
      " |      region is set, Update() will make sure the specified requested region\n",
      " |      is up-to-date. This is a confusing side effect to users who are just\n",
      " |      calling Update() on a filter. A first call to Update() will cause the\n",
      " |      largest possible region to be updated. A second call to Update() will\n",
      " |      update that same region. If a modification to the upstream pipeline\n",
      " |      cause a filter to have a different largest possible region, this\n",
      " |      second call to Update() will not cause the output requested region to\n",
      " |      be reset to the new largest possible region. Instead, the output\n",
      " |      requested region will be the same as the last time Update() was\n",
      " |      called. To have a filter always to produce its largest possible\n",
      " |      region, users should call UpdateLargestPossibleRegion() instead.\n",
      " |  \n",
      " |  UpdateLargestPossibleRegion = itkProcessObject_UpdateLargestPossibleRegion(...)\n",
      " |      itkProcessObject_UpdateLargestPossibleRegion(itkProcessObject self)\n",
      " |      \n",
      " |      Sets\n",
      " |      the output requested region to the largest possible region and\n",
      " |      updates.\n",
      " |      \n",
      " |      This is the method users should call if they want the entire dataset\n",
      " |      to be processed. If a user wants to update the same output region as a\n",
      " |      previous call to Update() or a previous call to\n",
      " |      UpdateLargestPossibleRegion(), then they should call the method\n",
      " |      Update().\n",
      " |  \n",
      " |  UpdateOutputData = itkProcessObject_UpdateOutputData(...)\n",
      " |      itkProcessObject_UpdateOutputData(itkProcessObject self, itkDataObject output)\n",
      " |      \n",
      " |      Actually generate\n",
      " |      new output\n",
      " |  \n",
      " |  UpdateOutputInformation = itkProcessObject_UpdateOutputInformation(...)\n",
      " |      itkProcessObject_UpdateOutputInformation(itkProcessObject self)\n",
      " |      \n",
      " |      Update the\n",
      " |      information describing the output data.\n",
      " |      \n",
      " |      This method transverses up the pipeline gathering modified time\n",
      " |      information. On the way back down the pipeline, this method calls\n",
      " |      GenerateOutputInformation() to set any necessary information about the\n",
      " |      output data objects. For instance, a filter that shrinks an image will\n",
      " |      need to provide an implementation for GenerateOutputInformation() that\n",
      " |      changes the spacing of the pixels. Such filters should call their\n",
      " |      superclass' implementation of GenerateOutputInformation prior to\n",
      " |      changing the information values they need (i.e.\n",
      " |      GenerateOutputInformation() should call\n",
      " |      Superclass::GenerateOutputInformation() prior to changing the\n",
      " |      information.\n",
      " |  \n",
      " |  UpdateProgress = itkProcessObject_UpdateProgress(...)\n",
      " |      itkProcessObject_UpdateProgress(itkProcessObject self, float progress)\n",
      " |      \n",
      " |      Update the progress\n",
      " |      of the process object.\n",
      " |      \n",
      " |      Sets the Progress ivar to amount and invokes any observers for the\n",
      " |      ProgressEvent. The parameter amount should be in [0,1] and is the\n",
      " |      cumulative (not incremental) progress.\n",
      " |  \n",
      " |  __call__(self, *args, **kargs)\n",
      " |      Change the inputs and attributes of the object and update it.\n",
      " |      \n",
      " |      The syntax is the same as the one used in New().\n",
      " |      UpdateLargestPossibleRegion() is ran once the input are changed, and\n",
      " |      the current object is returned, to make is easier to get one of the\n",
      " |      outputs. Something like 'filter(newInput, Threshold=10)[0]' would\n",
      " |      return the first output of the filter up to date.\n",
      " |  \n",
      " |  __getitem__(self, item)\n",
      " |      Returns the outputs of that object.\n",
      " |      \n",
      " |      The outputs are casted to their real type.\n",
      " |      Several outputs may be returned by using the slice notation.\n",
      " |  \n",
      " |  __len__(self)\n",
      " |      Returns the number of outputs of that object.\n",
      " |  \n",
      " |  ----------------------------------------------------------------------\n",
      " |  Methods inherited from ITKCommonBasePython.itkObject:\n",
      " |  \n",
      " |  AddObserver(self, *args) -> 'unsigned long'\n",
      " |      AddObserver(itkObject self, itkEventObject event, itkCommand arg1) -> unsigned long\n",
      " |      AddObserver(itkObject self, itkEventObject event, itkCommand arg1) -> unsigned long\n",
      " |      \n",
      " |      Allow people to\n",
      " |      add/remove/invoke observers (callbacks) to any ITK object. This is an\n",
      " |      implementation of the subject/observer design pattern. An observer is\n",
      " |      added by specifying an event to respond to and an itk::Command to\n",
      " |      execute. It returns an unsigned long tag which can be used later to\n",
      " |      remove the event or retrieve the command. The memory for the Command\n",
      " |      becomes the responsibility of this object, so don't pass the same\n",
      " |      instance of a command to two different objects\n",
      " |  \n",
      " |  DebugOff = itkObject_DebugOff(...)\n",
      " |      itkObject_DebugOff(itkObject self)\n",
      " |      \n",
      " |      Turn debugging output off.\n",
      " |  \n",
      " |  DebugOn = itkObject_DebugOn(...)\n",
      " |      itkObject_DebugOn(itkObject self)\n",
      " |      \n",
      " |      Turn debugging output on.\n",
      " |  \n",
      " |  GetCommand = itkObject_GetCommand(...)\n",
      " |      itkObject_GetCommand(itkObject self, unsigned long tag) -> itkCommand\n",
      " |      \n",
      " |      Get the command\n",
      " |      associated with the given tag. NOTE: This returns a pointer to a\n",
      " |      Command, but it is safe to assign this to a Command::Pointer. Since\n",
      " |      Command inherits from LightObject, at this point in the code, only a\n",
      " |      pointer or a reference to the Command can be used.\n",
      " |  \n",
      " |  GetDebug = itkObject_GetDebug(...)\n",
      " |      itkObject_GetDebug(itkObject self) -> bool\n",
      " |      \n",
      " |      Get the value of the debug\n",
      " |      flag.\n",
      " |  \n",
      " |  GetMTime = itkObject_GetMTime(...)\n",
      " |      itkObject_GetMTime(itkObject self) -> unsigned long\n",
      " |      \n",
      " |      Return this object's\n",
      " |      modified time.\n",
      " |  \n",
      " |  GetMetaDataDictionary = itkObject_GetMetaDataDictionary(...)\n",
      " |      GetMetaDataDictionary() -> itkMetaDataDictionary\n",
      " |      itkObject_GetMetaDataDictionary(itkObject self) -> itkMetaDataDictionary\n",
      " |      \n",
      " |      \n",
      " |      \n",
      " |      A constant reference to this objects MetaDataDictionary.\n",
      " |  \n",
      " |  GetObjectName = itkObject_GetObjectName(...)\n",
      " |      itkObject_GetObjectName(itkObject self) -> std::string const &\n",
      " |  \n",
      " |  GetTimeStamp = itkObject_GetTimeStamp(...)\n",
      " |      itkObject_GetTimeStamp(itkObject self) -> itkTimeStamp\n",
      " |      \n",
      " |      Return this object's\n",
      " |      time stamp.\n",
      " |  \n",
      " |  HasObserver = itkObject_HasObserver(...)\n",
      " |      itkObject_HasObserver(itkObject self, itkEventObject event) -> bool\n",
      " |      \n",
      " |      Return true if an\n",
      " |      observer is registered for this event.\n",
      " |  \n",
      " |  InvokeEvent = itkObject_InvokeEvent(...)\n",
      " |      InvokeEvent(itkEventObject arg0)\n",
      " |      itkObject_InvokeEvent(itkObject self, itkEventObject arg0)\n",
      " |      \n",
      " |      Call Execute on all the\n",
      " |      Commands observing this event id. The actions triggered by this call\n",
      " |      doesn't modify this object.\n",
      " |  \n",
      " |  Modified = itkObject_Modified(...)\n",
      " |      itkObject_Modified(itkObject self)\n",
      " |      \n",
      " |      Update the modification\n",
      " |      time for this object. Many filters rely on the modification time to\n",
      " |      determine if they need to recompute their data.\n",
      " |  \n",
      " |  RemoveAllObservers = itkObject_RemoveAllObservers(...)\n",
      " |      itkObject_RemoveAllObservers(itkObject self)\n",
      " |      \n",
      " |      Remove all\n",
      " |      observers .\n",
      " |  \n",
      " |  RemoveObserver = itkObject_RemoveObserver(...)\n",
      " |      itkObject_RemoveObserver(itkObject self, unsigned long tag)\n",
      " |      \n",
      " |      Remove the observer\n",
      " |      with this tag value.\n",
      " |  \n",
      " |  SetDebug = itkObject_SetDebug(...)\n",
      " |      itkObject_SetDebug(itkObject self, bool debugFlag)\n",
      " |      \n",
      " |      Set the value of the debug\n",
      " |      flag. A non-zero value turns debugging on.\n",
      " |  \n",
      " |  SetMetaDataDictionary = itkObject_SetMetaDataDictionary(...)\n",
      " |      itkObject_SetMetaDataDictionary(itkObject self, itkMetaDataDictionary rhs)\n",
      " |      \n",
      " |      Set the\n",
      " |      MetaDataDictionary\n",
      " |  \n",
      " |  SetObjectName = itkObject_SetObjectName(...)\n",
      " |      itkObject_SetObjectName(itkObject self, std::string const _arg)\n",
      " |      \n",
      " |      A facility to help\n",
      " |      application programmers set a human identifiable name for a given\n",
      " |      object. This has no inherent use in ITK, but is a convenience to allow\n",
      " |      developers to provide a name for this object.\n",
      " |  \n",
      " |  ----------------------------------------------------------------------\n",
      " |  Static methods inherited from ITKCommonBasePython.itkObject:\n",
      " |  \n",
      " |  GetGlobalWarningDisplay() -> 'bool'\n",
      " |      GetGlobalWarningDisplay() -> bool\n",
      " |  \n",
      " |  GlobalWarningDisplayOff() -> 'void'\n",
      " |      GlobalWarningDisplayOff()\n",
      " |  \n",
      " |  GlobalWarningDisplayOn() -> 'void'\n",
      " |      GlobalWarningDisplayOn()\n",
      " |  \n",
      " |  SetGlobalWarningDisplay(flag:'bool') -> 'void'\n",
      " |      SetGlobalWarningDisplay(bool flag)\n",
      " |  \n",
      " |  ----------------------------------------------------------------------\n",
      " |  Methods inherited from ITKCommonBasePython.itkLightObject:\n",
      " |  \n",
      " |  CreateAnother = itkLightObject_CreateAnother(...)\n",
      " |      itkLightObject_CreateAnother(itkLightObject self) -> itkLightObject_Pointer\n",
      " |      \n",
      " |      Create an object from\n",
      " |      an instance, potentially deferring to a factory. This method allows\n",
      " |      you to create an instance of an object that is exactly the same type\n",
      " |      as the referring object. This is useful in cases where an object has\n",
      " |      been cast back to a base class.\n",
      " |  \n",
      " |  GetNameOfClass = itkLightObject_GetNameOfClass(...)\n",
      " |      itkLightObject_GetNameOfClass(itkLightObject self) -> char const *\n",
      " |      \n",
      " |      Return the name of\n",
      " |      this class as a string. Used by the object factory (implemented in\n",
      " |      New()) to instantiate objects of a named type. Also used for debugging\n",
      " |      and other output information.\n",
      " |  \n",
      " |  GetReferenceCount = itkLightObject_GetReferenceCount(...)\n",
      " |      itkLightObject_GetReferenceCount(itkLightObject self) -> int\n",
      " |      \n",
      " |      Gets the\n",
      " |      reference count on this object.\n",
      " |  \n",
      " |  Print = itkLightObject_Print(...)\n",
      " |      Print(ostream os, itkIndent indent=0)\n",
      " |      itkLightObject_Print(itkLightObject self, ostream os)\n",
      " |      \n",
      " |      Cause the object to print\n",
      " |      itself out.\n",
      " |  \n",
      " |  __eq__ = itkLightObject___eq__(...)\n",
      " |      itkLightObject___eq__(itkLightObject self, itkLightObject obj) -> bool\n",
      " |  \n",
      " |  __hash__ = itkLightObject___hash__(...)\n",
      " |      itkLightObject___hash__(itkLightObject self) -> size_t\n",
      " |  \n",
      " |  __str__ = itkLightObject___str__(...)\n",
      " |      itkLightObject___str__(itkLightObject self) -> std::string\n",
      " |  \n",
      " |  ----------------------------------------------------------------------\n",
      " |  Static methods inherited from ITKCommonBasePython.itkLightObject:\n",
      " |  \n",
      " |  BreakOnError() -> 'void'\n",
      " |      BreakOnError()\n",
      " |  \n",
      " |  ----------------------------------------------------------------------\n",
      " |  Data descriptors inherited from ITKCommonBasePython.itkLightObject:\n",
      " |  \n",
      " |  __dict__\n",
      " |      dictionary for instance variables (if defined)\n",
      " |  \n",
      " |  __weakref__\n",
      " |      list of weak references to the object (if defined)\n",
      "\n"
     ]
    }
   ],
   "source": [
    "# Also observe the help with the Python `help` function\n",
    "input_image = itk.imread('data/BrainProtonDensitySlice.png', pixelType=itk.ctype('unsigned char'))\n",
    "confidence_connected = itk.ConfidenceConnectedImageFilter.New(input_image)\n",
    "help(confidence_connected)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {
    "slideshow": {
     "slide_type": "subslide"
    }
   },
   "outputs": [],
   "source": [
    "# Or, observe the help with IPython `?` operator\n",
    "confidence_connected?"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 72,
   "metadata": {
    "slideshow": {
     "slide_type": "subslide"
    }
   },
   "outputs": [],
   "source": [
    "# Set confidence connected filter parameters\n",
    "confidence_connected.SetMultiplier(2.3)\n",
    "confidence_connected.SetNumberOfIterations(5)\n",
    "confidence_connected.SetInitialNeighborhoodRadius(3)\n",
    "confidence_connected.SetReplaceValue(255)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {
    "slideshow": {
     "slide_type": "subslide"
    }
   },
   "outputs": [],
   "source": [
    "# What is the *Multiplier*?\n",
    "confidence_connected.SetMultiplier?"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "metadata": {
    "slideshow": {
     "slide_type": "subslide"
    }
   },
   "outputs": [
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "837e71d69fac4ca1bcdae658ad6d91e2",
       "version_major": 2,
       "version_minor": 0
      },
      "text/html": [
       "<p>Failed to display Jupyter Widget of type <code>Viewer</code>.</p>\n",
       "<p>\n",
       "  If you're reading this message in the Jupyter Notebook or JupyterLab Notebook, it may mean\n",
       "  that the widgets JavaScript is still loading. If this message persists, it\n",
       "  likely means that the widgets JavaScript library is either not installed or\n",
       "  not enabled. See the <a href=\"https://ipywidgets.readthedocs.io/en/stable/user_install.html\">Jupyter\n",
       "  Widgets Documentation</a> for setup instructions.\n",
       "</p>\n",
       "<p>\n",
       "  If you're reading this message in another frontend (for example, a static\n",
       "  rendering on GitHub or <a href=\"https://nbviewer.jupyter.org/\">NBViewer</a>),\n",
       "  it may mean that your frontend doesn't currently support widgets.\n",
       "</p>\n"
      ],
      "text/plain": [
       "Viewer(image=<itkImagePython.itkImageUC2; proxy of <Swig Object of type 'itkImageUC2 *' at 0x7effb97a6de0> >)"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# View the input image\n",
    "view(input_image)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 73,
   "metadata": {
    "slideshow": {
     "slide_type": "fragment"
    }
   },
   "outputs": [],
   "source": [
    "# Set the seed points\n",
    "Dimension = input_image.GetImageDimension()\n",
    "IndexType = itk.Index[Dimension]\n",
    "index = IndexType()\n",
    "\n",
    "index[0] = 100\n",
    "index[1] = 110\n",
    "\n",
    "confidence_connected.SetSeed(index)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 79,
   "metadata": {
    "slideshow": {
     "slide_type": "subslide"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.image.AxesImage at 0x7effb72f0208>"
      ]
     },
     "execution_count": 79,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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fKLfHZdfao/57qJZq71DNEFf0qp1qGfet8jt6GliVXf8c+/Efpc6nqIJ3Usf2N5T92AlcnF1/RPgbM2bZspejZoueQ2iWzCE0S+YQmiVzCM2SOYRmyRxCs2QOoVmy/wfTC8LV2Y+bAgAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7effb72fa7f0>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "confidence_connected.Update()\n",
    "\n",
    "plt.imshow(itk.GetArrayViewFromImage(confidence_connected.GetOutput()))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 80,
   "metadata": {
    "slideshow": {
     "slide_type": "subslide"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.image.AxesImage at 0x7effb88884e0>"
      ]
     },
     "execution_count": 80,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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RsxnPITRL5hCaJXMIzZI5hGbJHEKzZA6hWTKH0CzZ/wOp3JdcpoPNjQAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7effb7287630>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Pre-process to improve the result\n",
    "median = itk.MedianImageFilter.New(input_image)\n",
    "median.SetRadius(1)\n",
    "\n",
    "confidence_connected.SetInput(median)\n",
    "# Now, set a more \n",
    "confidence_connected.SetMultiplier(3.0)\n",
    "\n",
    "confidence_connected.Update()\n",
    "plt.imshow(itk.GetArrayViewFromImage(confidence_connected.GetOutput()))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "slideshow": {
     "slide_type": "subslide"
    }
   },
   "source": [
    "#### Fast Marching Segmentation\n",
    "\n",
    "When the differential equation governing the level set evolution has a very simple form, a fast evolution algorithm called fast marching can be used."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "slideshow": {
     "slide_type": "fragment"
    }
   },
   "source": [
    "Front propagation: $\\Delta x = V \\Delta t$\n",
    "\n",
    "![Fast marching speed image](data/fast-marching-speed-image.png)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "slideshow": {
     "slide_type": "subslide"
    }
   },
   "source": [
    "#### Fast Marching Time Crossing\n",
    "\n",
    "![Fast marching time crossing](data/fast-marching-time-crossing.png)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "slideshow": {
     "slide_type": "subslide"
    }
   },
   "source": [
    "First, let's smooth the input."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 114,
   "metadata": {
    "slideshow": {
     "slide_type": "fragment"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<itkTemplate itk::CurvatureAnisotropicDiffusionImageFilter>\n",
      "Options:\n",
      "  [<class 'itkImagePython.itkImageF2'>, <class 'itkImagePython.itkImageF2'>]\n",
      "  [<class 'itkImagePython.itkImageF3'>, <class 'itkImagePython.itkImageF3'>]\n"
     ]
    }
   ],
   "source": [
    "# What image types are supported by our smoothing filter?\n",
    "itk.CurvatureAnisotropicDiffusionImageFilter.GetTypes()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 115,
   "metadata": {
    "slideshow": {
     "slide_type": "fragment"
    }
   },
   "outputs": [],
   "source": [
    "# Cast to the supported image type\n",
    "FloatImageType = itk.Image[itk.ctype('float'), Dimension]\n",
    "caster = itk.CastImageFilter[type(input_image), FloatImageType].New()\n",
    "caster.SetInput(input_image)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 118,
   "metadata": {
    "slideshow": {
     "slide_type": "fragment"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.image.AxesImage at 0x7eff99d11e10>"
      ]
     },
     "execution_count": 118,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7eff98480b70>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "smoother = itk.CurvatureAnisotropicDiffusionImageFilter.New(caster)\n",
    "smoother.SetTimeStep(0.125)\n",
    "smoother.SetNumberOfIterations(5)\n",
    "smoother.SetConductanceParameter(3.0)\n",
    "smoother.Update()\n",
    "plt.imshow(itk.GetArrayViewFromImage(smoother.GetOutput()))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "slideshow": {
     "slide_type": "subslide"
    }
   },
   "source": [
    "Our speed image will be based off the local gradient magnitude."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 133,
   "metadata": {
    "slideshow": {
     "slide_type": "fragment"
    }
   },
   "outputs": [
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "7f50f88d609d4876830b5bea3a3c33ea",
       "version_major": 2,
       "version_minor": 0
      },
      "text/html": [
       "<p>Failed to display Jupyter Widget of type <code>Viewer</code>.</p>\n",
       "<p>\n",
       "  If you're reading this message in the Jupyter Notebook or JupyterLab Notebook, it may mean\n",
       "  that the widgets JavaScript is still loading. If this message persists, it\n",
       "  likely means that the widgets JavaScript library is either not installed or\n",
       "  not enabled. See the <a href=\"https://ipywidgets.readthedocs.io/en/stable/user_install.html\">Jupyter\n",
       "  Widgets Documentation</a> for setup instructions.\n",
       "</p>\n",
       "<p>\n",
       "  If you're reading this message in another frontend (for example, a static\n",
       "  rendering on GitHub or <a href=\"https://nbviewer.jupyter.org/\">NBViewer</a>),\n",
       "  it may mean that your frontend doesn't currently support widgets.\n",
       "</p>\n"
      ],
      "text/plain": [
       "Viewer(image=<itkImagePython.itkImageF2; proxy of <Swig Object of type 'itkImageF2 *' at 0x7effb6b43cf0> >)"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Note: we do not use `.New()`, which causes the filter to be created, `.Update()` called,\n",
    "# and the resulting image is returned\n",
    "gradient = itk.GradientMagnitudeRecursiveGaussianImageFilter(smoother, Sigma=1.0)\n",
    "view(gradient)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "slideshow": {
     "slide_type": "subslide"
    }
   },
   "source": [
    "For more information on the strategy for sigmoid parameter selection, see the [itk::SigmoidImageFilter documentation](https://itk.org/Doxygen/html/classitk_1_1SigmoidImageFilter.html) and the [discussion of the FastMarchingImageFilter in the ITK Software Guide](https://itk.org/ITKSoftwareGuide/html/Book2/ITKSoftwareGuide-Book2ch4.html#x37-1890004.3)."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 134,
   "metadata": {
    "slideshow": {
     "slide_type": "fragment"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.image.AxesImage at 0x7eff98ffa9b0>"
      ]
     },
     "execution_count": 134,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7effb714ef98>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "basin_minimum = 2.25\n",
    "border_minimum = 3.75\n",
    "\n",
    "alpha = - (border_minimum - basin_minimum) / 3.0\n",
    "beta = (border_minimum + basin_minimum) / 2.0\n",
    "\n",
    "sigmoid = itk.SigmoidImageFilter(gradient, OutputMinimum=0.0, OutputMaximum=1.0, Alpha=alpha, Beta=beta)\n",
    "plt.imshow(itk.GetArrayViewFromImage(sigmoid))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 146,
   "metadata": {
    "slideshow": {
     "slide_type": "subslide"
    }
   },
   "outputs": [],
   "source": [
    "fast_marching = itk.FastMarchingImageFilter.New(sigmoid)\n",
    "\n",
    "seed_position = IndexType()\n",
    "seed_position[0] = 100\n",
    "seed_position[1] = 110\n",
    "seed_value = 0.0\n",
    "NodeType = itk.LevelSetNode[itk.F,Dimension]\n",
    "node = NodeType()\n",
    "node.SetIndex(seed_position)\n",
    "node.SetValue(seed_value)\n",
    "\n",
    "NodeContainerType = itk.VectorContainer[itk.ctype('unsigned int'), NodeType]\n",
    "nodes = NodeContainerType.New()\n",
    "nodes.Initialize()\n",
    "nodes.InsertElement(0, node)\n",
    "fast_marching.SetTrialPoints(nodes)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 152,
   "metadata": {
    "slideshow": {
     "slide_type": "subslide"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.image.AxesImage at 0x7eff93935da0>"
      ]
     },
     "execution_count": 152,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
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      "text/plain": [
       "<matplotlib.figure.Figure at 0x7effb6afae80>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "fast_marching.SetStoppingValue(80)\n",
    "fast_marching.Update()\n",
    "plt.imshow(itk.GetArrayViewFromImage(fast_marching.GetOutput()))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 196,
   "metadata": {
    "slideshow": {
     "slide_type": "subslide"
    }
   },
   "outputs": [
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "fec5c11ebe3c4d8b9407faddf8e3fbaf",
       "version_major": 2,
       "version_minor": 0
      },
      "text/html": [
       "<p>Failed to display Jupyter Widget of type <code>interactive</code>.</p>\n",
       "<p>\n",
       "  If you're reading this message in the Jupyter Notebook or JupyterLab Notebook, it may mean\n",
       "  that the widgets JavaScript is still loading. If this message persists, it\n",
       "  likely means that the widgets JavaScript library is either not installed or\n",
       "  not enabled. See the <a href=\"https://ipywidgets.readthedocs.io/en/stable/user_install.html\">Jupyter\n",
       "  Widgets Documentation</a> for setup instructions.\n",
       "</p>\n",
       "<p>\n",
       "  If you're reading this message in another frontend (for example, a static\n",
       "  rendering on GitHub or <a href=\"https://nbviewer.jupyter.org/\">NBViewer</a>),\n",
       "  it may mean that your frontend doesn't currently support widgets.\n",
       "</p>\n"
      ],
      "text/plain": [
       "interactive(children=(FloatSlider(value=200.0, description='stopping_value', max=400.0, step=4.0), Output()), _dom_classes=('widget-interact',))"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/plain": [
       "<function __main__.show_arrival(stopping_value)>"
      ]
     },
     "execution_count": 196,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "def show_arrival(stopping_value):\n",
    "    fast_marching.SetStoppingValue(stopping_value)\n",
    "    fast_marching.Update()\n",
    "    fig, ax = plt.subplots(nrows=1, ncols=1)\n",
    "    ax.imshow(itk.GetArrayViewFromImage(fast_marching.GetOutput()))\n",
    "\n",
    "interact(show_arrival, stopping_value=(0.0,400.0,4.0))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 155,
   "metadata": {
    "slideshow": {
     "slide_type": "subslide"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.image.AxesImage at 0x7eff983f3860>"
      ]
     },
     "execution_count": 155,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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lcwjNkjmEZsn+H5LojlpQUsO+AAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7eff939662e8>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "thresholder = itk.BinaryThresholdImageFilter.New(fast_marching)\n",
    "time_threshold = 100\n",
    "thresholder.SetLowerThreshold(0.0)\n",
    "thresholder.SetUpperThreshold(time_threshold)\n",
    "thresholder.SetOutsideValue(0)\n",
    "thresholder.SetInsideValue(255)\n",
    "thresholder.Update()\n",
    "plt.imshow(itk.GetArrayViewFromImage(thresholder.GetOutput()))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 195,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "1e7c5b6e372243578f8f7002c3abeef7",
       "version_major": 2,
       "version_minor": 0
      },
      "text/html": [
       "<p>Failed to display Jupyter Widget of type <code>interactive</code>.</p>\n",
       "<p>\n",
       "  If you're reading this message in the Jupyter Notebook or JupyterLab Notebook, it may mean\n",
       "  that the widgets JavaScript is still loading. If this message persists, it\n",
       "  likely means that the widgets JavaScript library is either not installed or\n",
       "  not enabled. See the <a href=\"https://ipywidgets.readthedocs.io/en/stable/user_install.html\">Jupyter\n",
       "  Widgets Documentation</a> for setup instructions.\n",
       "</p>\n",
       "<p>\n",
       "  If you're reading this message in another frontend (for example, a static\n",
       "  rendering on GitHub or <a href=\"https://nbviewer.jupyter.org/\">NBViewer</a>),\n",
       "  it may mean that your frontend doesn't currently support widgets.\n",
       "</p>\n"
      ],
      "text/plain": [
       "interactive(children=(FloatSlider(value=200.0, description='stopping_value', max=400.0, step=4.0), Output()), _dom_classes=('widget-interact',))"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/plain": [
       "<function __main__.show_arrival(stopping_value)>"
      ]
     },
     "execution_count": 195,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "def show_arrival(stopping_value):\n",
    "    fast_marching.SetStoppingValue(stopping_value)\n",
    "    fast_marching.Update()\n",
    "    fig, ax = plt.subplots(nrows=1, ncols=1)\n",
    "    ax.imshow(itk.GetArrayViewFromImage(fast_marching.GetOutput()))\n",
    "\n",
    "interact(show_arrival, stopping_value=(0.0,400.0,4.0))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "slideshow": {
     "slide_type": "subslide"
    }
   },
   "source": [
    "#### Shape Detection Level Set\n",
    "\n",
    "![Shape detection level set](data/shape-detection-level-set.png)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "slideshow": {
     "slide_type": "subslide"
    }
   },
   "source": [
    "#### Shape Detection Level Set Pipeline\n",
    "\n",
    "\n",
    "![Shape detection level set pipeline](data/shape-detection-pipeline.png)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 202,
   "metadata": {
    "slideshow": {
     "slide_type": "subslide"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.image.AxesImage at 0x7eff935e5c18>"
      ]
     },
     "execution_count": 202,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7eff9804cf98>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Prepare the initial level set image\n",
    "binary_mask = itk.imread('data/WhiteMatter.png', pixelType=itk.ctype('float'))\n",
    "\n",
    "smoothing = itk.SmoothingRecursiveGaussianImageFilter.New(binary_mask)\n",
    "\n",
    "rescaler = itk.IntensityWindowingImageFilter[FloatImageType,FloatImageType].New(smoothing)\n",
    "rescaler.SetWindowMinimum(0)\n",
    "rescaler.SetWindowMaximum(255)\n",
    "# Invert the input level set to have negative values inside\n",
    "rescaler.SetOutputMinimum(5.0)\n",
    "rescaler.SetOutputMaximum(-5.0)\n",
    "\n",
    "rescaler.Update()\n",
    "plt.imshow(itk.GetArrayViewFromImage(rescaler))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 266,
   "metadata": {
    "slideshow": {
     "slide_type": "subslide"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.image.AxesImage at 0x7eff92747eb8>"
      ]
     },
     "execution_count": 266,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7eff92853e80>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Prepare the speed image\n",
    "gradient = itk.GradientMagnitudeRecursiveGaussianImageFilter(caster, Sigma=1.0)\n",
    "\n",
    "basin_minimum = 1\n",
    "border_minimum = 5.0\n",
    "\n",
    "alpha = - (border_minimum - basin_minimum) / 3.0\n",
    "beta = (border_minimum + basin_minimum) / 2.0\n",
    "\n",
    "sigmoid = itk.SigmoidImageFilter(gradient, OutputMinimum=0.0, OutputMaximum=1.0, Alpha=alpha, Beta=beta)\n",
    "plt.imshow(itk.GetArrayViewFromImage(sigmoid))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 273,
   "metadata": {
    "slideshow": {
     "slide_type": "subslide"
    }
   },
   "outputs": [],
   "source": [
    "shape_detection = itk.ShapeDetectionLevelSetImageFilter.New(rescaler)\n",
    "shape_detection.SetFeatureImage(sigmoid)\n",
    "shape_detection.SetMaximumRMSError(0.001)\n",
    "shape_detection.SetNumberOfIterations(100)\n",
    "shape_detection.SetPropagationScaling(1.0)\n",
    "shape_detection.SetCurvatureScaling(2.0)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 274,
   "metadata": {
    "slideshow": {
     "slide_type": "fragment"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.image.AxesImage at 0x7eff924aada0>"
      ]
     },
     "execution_count": 274,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7eff92640390>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "thresholder = itk.BinaryThresholdImageFilter.New(shape_detection)\n",
    "thresholder.SetLowerThreshold(-1e7)\n",
    "# Cut at the zero set\n",
    "thresholder.SetUpperThreshold(0.0)\n",
    "thresholder.SetOutsideValue(0)\n",
    "thresholder.SetInsideValue(255)\n",
    "thresholder.Update()\n",
    "plt.imshow(itk.GetArrayViewFromImage(thresholder.GetOutput()))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "slideshow": {
     "slide_type": "slide"
    }
   },
   "source": [
    "## Exercises"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "slideshow": {
     "slide_type": "subslide"
    }
   },
   "source": [
    "### Exercise 1: ConnectedThresholdImageFilter vs ConfidenceConnectedmageFilter\n",
    "\n",
    "Use [itk::ConnectedThresholdImageFilter](https://itk.org/Doxygen/html/classitk_1_1ConnectedThresholdImageFilter.html) instead of *itk::ConfidenceConnectedImageFilter*.\n",
    "\n",
    "- Can the brain's ventricle be segmented with this filter?\n",
    "\n",
    "**Hint:** Instead of `SetMultiplier()`, `SetInitialNeighborhoodRadius()`, and `SetNumberOfIterations()`, the new filter has methods `SetLower()` and `SetUpper()`."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "slideshow": {
     "slide_type": "fragment"
    }
   },
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "slideshow": {
     "slide_type": "subslide"
    }
   },
   "source": [
    "### Exercise 2: IsolatedConnectedImageFilter vs ConfidenceConnectedmageFilter\n",
    "\n",
    "Use [itk::IsolatedConnectedImageFilter](https://itk.org/Doxygen/html/classitk_1_1IsolatedConnectedImageFilter.html) instead of *itk::ConfidenceConnectedImageFilter*.\n",
    "\n",
    "- Can the brain's ventricle be segmented with this filter?\n",
    "\n",
    "**Hint:** Instead of `SetMultiplier()`, `SetInitialNeighborhoodRadius()`, `SetSeed()`, and `NumberOfIterations()`, the new filter has methods `SetSeed1()`, `SetSeed2()`, `SetUpperValueLimit()`, `FindUpperThresholdOff()`."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "slideshow": {
     "slide_type": "fragment"
    }
   },
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "slideshow": {
     "slide_type": "subslide"
    }
   },
   "source": [
    "### Exercise 3: Level Set Curvature and Propagation Terms\n",
    "\n",
    "Investigate the following parameters for the shape detection level set image filter.\n",
    "\n",
    "| Min Basin | Min Boundary | Propagation Scaling | Curvature Scaling | Number of Iterations |\n",
    "|-----------|--------------|---------------------|-------------------|----------------------|\n",
    "| 1.0       | 5.0          | 1.0                 | 2.0               | 100                  |\n",
    "| 1.0       | 5.0          | 1.0                 | 2.0               | 500                  |\n",
    "| 1.0       | 5.0          | 1.0                 | 4.0               | 100                  |\n",
    "| 1.0       | 20.0         | 1.0                 | 1.0               | 100                  |\n",
    "\n",
    "- What explains the observed behavior?\n",
    "- How could we stop the level set from propagating too far?"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "slideshow": {
     "slide_type": "fragment"
    }
   },
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "slideshow": {
     "slide_type": "subslide"
    }
   },
   "source": [
    "### Enjoy ITK!"
   ]
  }
 ],
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