WK2_Image_Analysis_Task.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Image Analysis Task"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "In this programming task, we'll get to work with some sample medical imaging data in the DICOM format and we'll learn how to visualise it and how to do some basic medical image analysis tasks, such as smoothing and segmentation. \n",
    "\n",
    "<div class=\"alert alert-block alert-info\">\n",
    "**Important Note**: The code for this task is quite complex.  You are not expected to fully understand the code below, but just to try your hand at it, so that you get an idea of what it is like to handle medical imaging data.  You are welcome to simply run the code without spending time figuring out what it does. Explanations are provided for the curious ones!\n",
    "</div>"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Part 1: Importing packages needed"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "*Pydicom* is a Python package for working with DICOM files.  It allows us to read and easily manipulate DICOM files with the use of Python.\n",
    "\n",
    "ITK (Insight Segmentation and Registration Toolkit) is a library that enables image processing, and it is widely used for the development of image segmentation and image registration programs. Here, we'll use *SimpleITK*, as it is a simplified interface to ITK.\n",
    "\n",
    "The *OS* module in Python provides a way of using operating system dependent functionality, for instance for working with files and folders.  \n",
    "\n",
    "As already discussed in Week 1, *NumPy* is a widely used Python package for working with large, multi-dimensional data (note that here we'll be using it to manipulate pixel data), while *Matplotlib* is a Python library for plotting data. \n",
    "\n",
    "Now, lets import the packages. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Populating the interactive namespace from numpy and matplotlib\n"
     ]
    }
   ],
   "source": [
    "import pydicom\n",
    "import SimpleITK\n",
    "import os\n",
    "import numpy\n",
    "import matplotlib.pyplot as plt\n",
    "%pylab inline"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<br>\n",
    "## Part 2: Loading the data"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "In this programming task we're going to be working with a dataset from an MR examination of a patient's head. The images are captured as DICOM files and they are stored in a folder called 'MyHead'.  "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Run the code in the following cell to create a list called 'lstFilesDCM', which contains all DICOM files within 'MyHead'. <br>\n",
    "\n",
    "Remember that you are not expected to understand all the code in this programming task. (If, however, you are really curious, this is what this code does: It first creates a list called 'lstFilesDCM' that is initially empty. It then uses the *os.walk()* function from the OS module to traverse the MyHead folder. For each file found in this folder, we check whether it is a DICOM file, and if it is, we add its extended name (i.e. the folder and file name) to the lstFilesDCM list.)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "PathDicom = \"./readonly/MyHead/\"\n",
    "lstFilesDCM = []  # create an empty list\n",
    "for dirName, subdirList, fileList in os.walk(PathDicom):\n",
    "    for filename in fileList:\n",
    "        if \".dcm\" in filename.lower():  # check whether the file is DICOM\n",
    "            lstFilesDCM.append(os.path.join(dirName,filename))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Run the code in the following cell to get the first five elements of the 'lstFilesDCM' list, i.e. the extended names of the first five DICOM files."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "['./readonly/MyHead/MR000096.dcm',\n",
       " './readonly/MyHead/MR000078.dcm',\n",
       " './readonly/MyHead/MR000159.dcm',\n",
       " './readonly/MyHead/MR000069.dcm',\n",
       " './readonly/MyHead/MR000087.dcm']"
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "lstFilesDCM[:5]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "176"
      ]
     },
     "execution_count": 19,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "len(lstFilesDCM)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Reading a DICOM file"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Run the code in the following cell to use Pydicom's *read_file()* function to read the first DICOM file within lstFilesDCM (identified as 'lstFilesDCM[0]'). The resulting object is called 'HeadDs'. Note that we could have called it anything we wanted."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [],
   "source": [
    "HeadDs = pydicom.read_file(lstFilesDCM[0])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Getting metadata"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We can now get metadata of interest for this image with the use of appropriate Pydicom functions (e.g. *PatientPosition* or *Modality*).\n",
    "\n",
    "Run the code in the following cell to get the position of the patient relative to the imaging equipment space."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "'HFS'"
      ]
     },
     "execution_count": 21,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "HeadDs.PatientPosition"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "*HFS* stands for Head First-Supine. This means that the patient’s head was positioned toward the front of the imaging equipment and it was in an upward direction. "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Run the code in the following cell to get the date the study started. Note that the date format is YYYYMMDD."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "'20111110'"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "HeadDs.StudyDate"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Run the code in the following cell to get the image modality. Note that 'MR' stands for Magnetic Resonance, 'CT' stands for Computed Tomography and 'PT' stands for Positron Emission Tomography (PET)."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "'MR'"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "HeadDs.Modality"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<br>\n",
    "## Part 3: Visualisation"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Now that we've got an idea of the imaging data that we're going to be working with, we'll visualise the data by creating a two-dimensional plot. But first we'll need to extract some additional information needed for plotting.\n",
    "\n",
    "**Important note**: Remember that you don't need to fully understand the code in this programming task. In fact, you could simply reuse the code in Part 3 for a new collection of MR images, given that you've called the list of your DICOM files 'lstFilesDCM' and the first image 'HeadDs'."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Preparing for visualisation"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "In order to plot the data with Matplotlib, we first need to i) combine the pixel data from all DICOM files (i.e. from all slices) into a 3D dataset, and ii) specify appropriate coordinate axes."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We'll start by calculating the total dimensions of the combined 3D dataset. These should be: (Number of pixel rows in a slice) x (Number of pixel columns in a slice) x (Number of slices) along the x, y, and z cartesian axes. We'll call the calculated dimensions 'CalcPixelDims'."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [],
   "source": [
    "CalcPixelDims = (int(HeadDs.Rows), int(HeadDs.Columns), len(lstFilesDCM))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Run the code in the following cell to get CalcPixelDims."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(256, 256, 176)"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "CalcPixelDims"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Now that we've calculated the dimensions, we can create a 3D NumPy array to address the first point discussed above. Note that a NumPy array is understood as a grid of values, all of the same type. \n",
    "\n",
    "The following code creates a NumPy array called 'HeadImgArray' that has the same size as CalcPixelDims, and which contains the pixel data from all DICOM files in lstFilesDCM. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [],
   "source": [
    "HeadImgArray = numpy.zeros(CalcPixelDims, dtype=HeadDs.pixel_array.dtype)\n",
    "\n",
    "for filenameDCM in lstFilesDCM:\n",
    "    ds = pydicom.read_file(filenameDCM)\n",
    "    HeadImgArray[:, :, lstFilesDCM.index(filenameDCM)] = ds.pixel_array"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Now that we've addressed the first point, we move on to specify appropriate coordinate axes.\n",
    "\n",
    "We use Pydicom's *PixelSpacing* and *SliceThickness* functions to calculate the spacing between pixels in the three axes. We call the resulting object 'CalcPixelSpacing'."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [],
   "source": [
    "CalcPixelSpacing = (float(HeadDs.PixelSpacing[0]), float(HeadDs.PixelSpacing[1]), float(HeadDs.SliceThickness))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Then, we use CalcPixelDims and CalcPixelSpacing to calculate coordinate axes. You don't need to understand what this code does. Just run it to compute x, y and z."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [],
   "source": [
    "x = numpy.arange(0.0, (CalcPixelDims[0]+1)*CalcPixelSpacing[0], CalcPixelSpacing[0])\n",
    "y = numpy.arange(0.0, (CalcPixelDims[1]+1)*CalcPixelSpacing[1], CalcPixelSpacing[1])\n",
    "z = numpy.arange(0.0, (CalcPixelDims[2]+1)*CalcPixelSpacing[2], CalcPixelSpacing[2])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Visualising"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We can now plot the data with the use of the pyplot module in matplotlib.\n",
    "\n",
    "Again, you don't need to understand in detail what this code does. You can simply run it to get a plot of the head MR image. Note that you can modify the number in the last line of the code to get a different slice of the head."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.collections.QuadMesh at 0x7f485c6916a0>"
      ]
     },
     "execution_count": 13,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
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\n",
      "text/plain": [
       "<Figure size 1800x1200 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(dpi=300)\n",
    "plt.axes().set_aspect('equal', 'datalim')\n",
    "plt.set_cmap(plt.gray())\n",
    "plt.pcolormesh(x, y, numpy.flipud(HeadImgArray[:, :, 125]))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<br>\n",
    "## Part 4: Segmentation"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "In this part of the notebook we'll use SimpleITK to segment the white and grey matter in the MR images."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Specifying a helper function"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We'll be visualising quite a few 2D SimpleITK images in this part, so here we'll define a function that quickly plots a 2D SimpleITK image with a greyscale colourmap and accompanying axes.  \n",
    "\n",
    "There's no need to understand what this code does - you can simply run it so that you can use the *sitk_show* function later on."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [],
   "source": [
    "def sitk_show(img, title=None, margin=0.05, dpi=40 ):\n",
    "    nda = SimpleITK.GetArrayFromImage(img)\n",
    "    spacing = img.GetSpacing()\n",
    "    figsize = (1 + margin) * nda.shape[0] / dpi, (1 + margin) * nda.shape[1] / dpi\n",
    "    extent = (0, nda.shape[1]*spacing[1], nda.shape[0]*spacing[0], 0)\n",
    "    fig = plt.figure(figsize=figsize, dpi=dpi)\n",
    "    ax = fig.add_axes([margin, margin, 1 - 2*margin, 1 - 2*margin])\n",
    "\n",
    "    plt.set_cmap(\"gray\")\n",
    "    ax.imshow(nda,extent=extent,interpolation=None)\n",
    "    \n",
    "    if title:\n",
    "        plt.title(title)\n",
    "    \n",
    "    plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Loading the data in SimpleITK"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We now need to tell SimpleITK to read the DICOM files within the 'MyHead' folder. Run the code in the following cell to extract the contents of the MRI dataset and create the corresponding 3D image called 'img3DOriginal'. \n",
    "\n",
    "Note that you don't need to understand what this code does. You can simply run it, so as to use img3DOriginal later on.  You can also reuse this code for a new collection of MR images, given that you've called the location of your DICOM files 'PathDicom'."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [],
   "source": [
    "reader = SimpleITK.ImageSeriesReader()\n",
    "filenamesDICOM = reader.GetGDCMSeriesFileNames(PathDicom)\n",
    "reader.SetFileNames(filenamesDICOM)\n",
    "img3DOriginal = reader.Execute()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "In order to keep things simple, we'll segment a 2D slice of the 3D image (rather than the entire 3D image). Run the code below to specify that we want Z-slice 50 of the 3D image. We'll call this slice 'imgOriginal'.\n",
    "\n",
    "Note that you could ask for a different Z-slice, if you want."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [],
   "source": [
    "imgOriginal = img3DOriginal[:,:,50]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Visualising the original data"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We'll now call our *sitk_show* function to visualise the original data. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 268.8x268.8 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "sitk_show(imgOriginal)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Smoothing"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Smoothing is the process of reducing noise within an image or producing a less pixelated image.  The result is an image with sharp edges or boundaries preserved and smoothing occurring only within a region.\n",
    "\n",
    "In our case, we can see that the original image data exhibits quite a bit of noise, which is rather typical of MRI datasets. We will reduce the noise, so as to ease the process of segmentation later on.\n",
    "\n",
    "Run the code in the following cell to apply a Curvature Flow Image Filter to smoothen *imgOriginal*. We'll call the resulting image 'imgSmooth'."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "imgSmooth = SimpleITK.CurvatureFlow(image1=imgOriginal,\n",
    "                                    timeStep=0.125,\n",
    "                                    numberOfIterations=5)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Now, lets see the results of the smoothened image."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "sitk_show(imgSmooth)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Segmentation with the ConnectedThreshold filter"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We will now apply region growing techniques to segment the white and grey matter of the smoothened image."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Initial segmentation of the white matter"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Run the code in the following cell to apply SimpleITK's *ConnectedThreshold* filter function to imgSmooth. The image of the resulting segmented white matter is called 'imgWhiteMatter'.\n",
    "\n",
    "As with other parts of this programming task, you don't need to fully understand what this code does. You can simply run it to get the initial segmentation of the white matter. \n",
    "\n",
    "If, however, you're really curious, here's a brief explanation: This filter starts from a seed point in the image that we know is white matter, and looks at all connected pixels. If their values lie within a range of interest, then they are labelled as white matter. In our case, we've set the seed to be point (150, 75), as by inspecting the previous image, it looked like that point was indeed white matter. We've also set the range of values to be between 130 and 190, as these were roughly the values that the white matter pixels exhibited (this was inspected with the use of a separate DICOM viewing software)."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "lstSeeds = [(150,75)]\n",
    "\n",
    "imgWhiteMatter = SimpleITK.ConnectedThreshold(image1=imgSmooth, \n",
    "                                              seedList=lstSeeds, \n",
    "                                              lower=130, \n",
    "                                              upper=190,\n",
    "                                              replaceValue=1)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Next, we want to view the result of the segmentation. If we tried to visualise imgWhiteMatter, then all we would see would be a white-colour label in a black backdrop, which doesn’t give us much insight.  However, if we use a label overlay, then we can see the result of the segmentation of white matter in a basic RGB (red, green, blue) colour. In our case, it will show as green. \n",
    "\n",
    "Note that before overlaying imgSmooth and imgWhiteMatter, we first need to manipulate imgSmooth so we can successfully mix the two images. This is what the following code does. The resulting image is called 'imgSmoothInt'."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "imgSmoothInt = SimpleITK.Cast(SimpleITK.RescaleIntensity(imgSmooth), imgWhiteMatter.GetPixelID())"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Now let's use SimpleITK's *LabelOverlay* function to overlay 'imgSmoothInt' and 'imgWhiteMatter', and let's visualise the result."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "sitk_show(SimpleITK.LabelOverlay(imgSmoothInt, imgWhiteMatter))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Hole-filling of the segmented white matter"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "As you can see in the figure above, there are several holes in the segmented white matter. We'll rectify this by applying hole-filling.  \n",
    "\n",
    "Run the code in the following cell to use SimpleITK's *VotingBinaryHoleFilling* filter to fill in holes in *imgWhiteMatter*.  The resulting image is called 'imgWhiteMatterNoHoles'."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "imgWhiteMatterNoHoles = SimpleITK.VotingBinaryHoleFilling(image1=imgWhiteMatter,\n",
    "                                                          radius=[2]*3,\n",
    "                                                          majorityThreshold=1,\n",
    "                                                          backgroundValue=0,\n",
    "                                                          foregroundValue=1)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Now let's use SimpleITK's *LabelOverlay* function to overlay 'imgSmoothInt' and the newly obtained 'imgWhiteMatterNoHoles', and let's visualise the result."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "sitk_show(SimpleITK.LabelOverlay(imgSmoothInt, imgWhiteMatterNoHoles))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Segmentation and hole-filling of grey matter"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We'll now repeat the process for grey matter. In other words, we'll do some preliminary segmentation and then we'll perform hole-filling of the segmented grey matter. We'll then overlay the 'imgSmoothInt' and the 'imgGreyMatterNoHoles' images and we'll visualise the result."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "lstSeeds = [(119, 83), (198, 80), (185, 102), (164, 43)]\n",
    "\n",
    "imgGreyMatter = SimpleITK.ConnectedThreshold(image1=imgSmooth, \n",
    "                                             seedList=lstSeeds, \n",
    "                                             lower=150, \n",
    "                                             upper=270,\n",
    "                                             replaceValue=2)\n",
    "\n",
    "imgGreyMatterNoHoles = SimpleITK.VotingBinaryHoleFilling(image1=imgGreyMatter,\n",
    "                                                         radius=[2]*3,\n",
    "                                                         majorityThreshold=1,\n",
    "                                                         backgroundValue=0,\n",
    "                                                         foregroundValue=2) # labelGrayMatter\n",
    "\n",
    "sitk_show(SimpleITK.LabelOverlay(imgSmoothInt, imgGreyMatterNoHoles))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Combining the white and grey matter"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Lastly, we want to combine the two label-fields, in other words, the white and grey matter.  We do this by running the code below, with the results being stored under 'imgLabels'.   "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true,
    "scrolled": true
   },
   "outputs": [],
   "source": [
    "imgLabels = imgWhiteMatterNoHoles | imgGreyMatterNoHoles"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Now let's use SimpleITK's *LabelOverlay* function to overlay 'imgSmoothInt' and the newly obtained 'imgLabels', and let's visualise the result."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "sitk_show(SimpleITK.LabelOverlay(imgSmoothInt, imgLabels))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Note that the cyan-coloured label are regions where both the white matter and grey matter overlap from the initial segmentation process above.  \n",
    "\n",
    "The majority of those regions should actually be part of the grey matter. Run the code in the following cell to fix this, with the results being stored under 'imgLabels2'. Note that you don't need to understand in detail what the code does. All you need to know is that it assigns any overlapping regions to the grey matter."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "imgMask = (imgWhiteMatterNoHoles/1) * (imgGreyMatterNoHoles/2)\n",
    "imgMask2 = SimpleITK.Cast(imgMask, imgWhiteMatterNoHoles.GetPixelIDValue())\n",
    "imgWhiteMatterNoHoles = imgWhiteMatterNoHoles - (imgMask2*1)\n",
    "imgLabels2 = imgWhiteMatterNoHoles + imgGreyMatterNoHoles"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Now let's use SimpleITK's *LabelOverlay* function to overlay 'imgSmoothInt' and the newly obtained 'imgLabels2', and let's visualise the result."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "sitk_show(SimpleITK.LabelOverlay(imgSmoothInt, imgLabels2))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "And that's it! You've now had a taste of medical image processing!\n",
    "\n",
    "You're welcome to play around with this code. If you have any questions or comments, please post them in the forums."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": []
  }
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