--- a
+++ b/lung_segmentation.ipynb
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+{
+ "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\")"
+   ]
+  }
+ ],
+ "metadata": {
+  "kernelspec": {
+   "display_name": "Python 2",
+   "language": "python",
+   "name": "python2"
+  },
+  "language_info": {
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 2
+   },
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
+   "name": "python",
+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython2",
+   "version": "2.7.13"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 1
+}