{

 "cells": [

  {

   "cell_type": "markdown",

   "metadata": {},

   "source": [

    "# Lung Lobes Segmentation"

   ]

  },

  {

   "cell_type": "markdown",

   "metadata": {},

   "source": [

    "## Imports"

   ]

  },

  {

   "cell_type": "code",

   "execution_count": null,

   "metadata": {},

   "outputs": [],

   "source": [

    "%matplotlib inline\n",

    "\n",

    "import SimpleITK as sitk\n",

    "import numpy as np\n",

    "import matplotlib.pyplot as plt\n",

    "import scipy as sp \n",

    "import gui\n",

    "import cv2\n",

    "import matplotlib.image as mpimg\n",

    "\n",

    "# from mayavi import mlab\n",

    "from scipy import signal\n",

    "from myshow import myshow, myshow3d\n",

    "from read_data import LoadData\n",

    "from lung_segment import LungSegment\n",

    "from vessel_segment import VesselSegment\n",

    "from mpl_toolkits.mplot3d import Axes3D"

   ]

  },

  {

   "cell_type": "markdown",

   "metadata": {},

   "source": [

    "## Read data"

   ]

  },

  {

   "cell_type": "code",

   "execution_count": null,

   "metadata": {

    "collapsed": true

   },

   "outputs": [],

   "source": [

    "# loading data\n",

    "data_path = \"resource/\"\n",

    "img_name = \"lola11-01.mhd\"\n",

    "data = LoadData(data_path, img_name)\n",

    "data.loaddata()"

   ]

  },

  {

   "cell_type": "code",

   "execution_count": null,

   "metadata": {

    "collapsed": true

   },

   "outputs": [],

   "source": [

    "print \"the shape of image is \", data.image.GetSize()"

   ]

  },

  {

   "cell_type": "markdown",

   "metadata": {},

   "source": [

    "## Lung Segmentation\n",

    "Rescale the intensities and map them to [0,255]"

   ]

  },

  {

   "cell_type": "code",

   "execution_count": null,

   "metadata": {},

   "outputs": [],

   "source": [

    "% matplotlib notebook\n",

    "\n",

    "WINDOW_LEVEL = (1050,500)\n",

    "ls = LungSegment(data.image)\n",

    "\n",

    "# Convert image to uint8 for showing \n",

    "ls.conv_2_uint8(WINDOW_LEVEL)\n",

    "\n",

    "# Set the seed point manually...\n",

    "seed_pts = [(125,237,200), (369,237,200)]\n",

    "\n",

    "# Compute region growing\n",

    "ls.regiongrowing(seed_pts)\n",

    "\n",

    "# showimg image\n",

    "ls.image_showing(\"Region Growing Result\")"

   ]

  },

  {

   "cell_type": "markdown",

   "metadata": {},

   "source": [

    "Write the region growing image"

   ]

  },

  {

   "cell_type": "code",

   "execution_count": null,

   "metadata": {

    "collapsed": true

   },

   "outputs": [],

   "source": [

    "sitk.WriteImage(ls.temp_img, \"seg_implicit_thresholds.mhd\")"

   ]

  },

  {

   "cell_type": "code",

   "execution_count": null,

   "metadata": {},

   "outputs": [],

   "source": [

    "# Morphological Operatinon (Closing)\n",

    "ls.image_closing(7)\n",

    "\n",

    "# write image\n",

    "sitk.WriteImage(ls.temp_img, \"img_closing.mhd\")"

   ]

  },

  {

   "cell_type": "code",

   "execution_count": null,

   "metadata": {},

   "outputs": [],

   "source": [

    "img_closing = sitk.ReadImage(\"img_closing.mhd\") # reading the existed closing image \n",

    "\n",

    "# get the numpy array of the 3D closing image for future using\n",

    "img_closing_ndarray = sitk.GetArrayFromImage(img_closing)"

   ]

  },

  {

   "cell_type": "markdown",

   "metadata": {},

   "source": [

    "## Vasculature Segmentation"

   ]

  },

  {

   "cell_type": "code",

   "execution_count": null,

   "metadata": {},

   "outputs": [],

   "source": [

    "# get the result of previous lung segmentation.\n",

    "img_closing_ndarray = sitk.GetImageFromArray(img_closing_ndarray)"

   ]

  },

  {

   "cell_type": "code",

   "execution_count": null,

   "metadata": {},

   "outputs": [],

   "source": [

    "vs = VesselSegment(original=data.image, closing=img_closing_ndarray)"

   ]

  },

  {

   "cell_type": "code",

   "execution_count": null,

   "metadata": {},

   "outputs": [],

   "source": [

    "print \"   Pricessing Generate lung mask...\"\n",

    "vs.generate_lung_mask(lunglabel=[1,-5000], offset = 0)\n",

    "\n",

    "# Write image...\n",

    "Lung_mask = sitk.GetImageFromArray(vs.img)\n",

    "sitk.WriteImage(Lung_mask, \"Lung_mask.mhd\")\n",

    "\n",

    "print \"   Processing Downsampling...\"\n",

    "vs.downsampling()\n",

    "\n",

    "print \"   Processing Thresholding...\"\n",

    "vs.thresholding(thval=180)\n",

    "down = sitk.GetImageFromArray(vs.temp_img)\n",

    "sitk.WriteImage(down, \"downsample.mhd\")\n",

    "\n",

    "print \"   Processing Region Growing...\"\n",

    "vs.max_filter(filter_size=5)"

   ]

  },

  {

   "cell_type": "code",

   "execution_count": null,

   "metadata": {},

   "outputs": [],

   "source": [

    "# save the vasculature-segmented image\n",

    "filtered = sitk.GetImageFromArray(vs.temp_img)\n",

    "sitk.WriteImage(filtered, \"filtered.mhd\")"

   ]

  },

  {

   "cell_type": "code",

   "execution_count": null,

   "metadata": {},

   "outputs": [],

   "source": [

    "# convert to binary image\n",

    "filtered = sitk.ReadImage(\"filtered.mhd\")\n",

    "filtered = sitk.GetArrayFromImage(filtered)\n",

    "filtered[filtered > 0] = 1\n",

    "binary_filtered = sitk.GetImageFromArray(filtered)\n",

    "sitk.WriteImage(binary_filtered, \"binary_filtered.mhd\")"

   ]

  },

  {

   "cell_type": "markdown",

   "metadata": {

    "collapsed": true

   },

   "source": [

    "## Postprocessing for fissure enhancement\n",

    "**Note:** the following steps need the result of fissure segmentation obtained by the C++ codes I provide. Since the SimpleITK package didn't provide enough functions for fissure segmentation (like computing 3D Hessian matrix), I used ITK C++ for this part, instead."

   ]

  },

  {

   "cell_type": "code",

   "execution_count": null,

   "metadata": {},

   "outputs": [],

   "source": [

    "import SimpleITK as sitk\n",

    "from read_data import LoadData\n",

    "import numpy as np\n",

    "import collections\n",

    "\n",

    "# Load the fissure image\n",

    "data = LoadData(path=\"fissure_enhancement_cxx/\", name=\"vessel_rg.mhd\")\n",

    "data.loaddata()\n",

    "image = sitk.GetArrayFromImage(data.image)"

   ]

  },

  {

   "cell_type": "code",

   "execution_count": null,

   "metadata": {},

   "outputs": [],

   "source": [

    "# count the volume for each label and remove the ones less than 5000.\n",

    "nonzeros = image[image > 0]\n",

    "d = collections.Counter( nonzeros )\n",

    "val_key = []\n",

    "keys = set([])\n",

    "for key, val in d.items():\n",

    "    if val > 5000:\n",

    "        keys.add(key)\n",

    "\n",

    "image[image == 0] = 1\n",

    "for key in keys:\n",

    "    image[image == key] = 0\n",

    "\n",

    "image[image > 0] = 2\n",

    "image[image == 0] = 1 # the regions left are set to 1\n",

    "image[image == 2] = 0 # rest is 0\n",

    "img = sitk.GetImageFromArray(image.astype(np.uint8))"

   ]

  },

  {

   "cell_type": "code",

   "execution_count": null,

   "metadata": {

    "collapsed": true

   },

   "outputs": [],

   "source": [

    "# Using closing to fill holes\n",

    "size = 7\n",

    "closing = sitk.BinaryMorphologicalClosingImageFilter()\n",

    "closing.SetForegroundValue(255)\n",

    "closing.SetKernelRadius(size)\n",

    "img = closing.Execute(img)\n",

    "# save results\n",

    "sitk.WriteImage(img, \"fissure_enhancement_cxx/voxel_val_region_growing_closing.mhd\")"

   ]

  },

  {

   "cell_type": "markdown",

   "metadata": {},

   "source": [

    "## Generate Label map for lung, vasculature and fissure regions"

   ]

  },

  {

   "cell_type": "code",

   "execution_count": null,

   "metadata": {

    "collapsed": true

   },

   "outputs": [],

   "source": [

    "lung_mask = LoadData(path=\"\", name=\"Lung_mask.mhd\")\n",

    "lung_mask.loaddata()\n",

    "fissure = LoadData(path=\"fissure_enhancement_cxx/\", name=\"voxel_val_region_growing_closing.mhd\")\n",

    "fissure.loaddata()\n",

    "vessel = LoadData(path=\"\", name=\"binary_filtered.mhd\")\n",

    "vessel.loaddata()"

   ]

  },

  {

   "cell_type": "code",

   "execution_count": null,

   "metadata": {

    "collapsed": true

   },

   "outputs": [],

   "source": [

    "lung_mask = sitk.GetArrayFromImage(lung_mask.image)\n",

    "fissure = sitk.GetArrayFromImage(fissure.image)\n",

    "vessel = sitk.GetArrayFromImage(vessel.image)"

   ]

  },

  {

   "cell_type": "code",

   "execution_count": null,

   "metadata": {

    "collapsed": true

   },

   "outputs": [],

   "source": [

    "lung_mask[lung_mask != 0] = 3\n",

    "lung_mask[vessel > 0] = 1\n",

    "lung_mask[fissure > 0] = 2"

   ]

  },

  {

   "cell_type": "code",

   "execution_count": null,

   "metadata": {

    "collapsed": true

   },

   "outputs": [],

   "source": [

    "lung_mask = sitk.GetImageFromArray(lung_mask)\n",

    "sitk.WriteImage(lung_mask, \"label_map.mhd\")"

   ]

  }

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