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jonasteuwenjonasteuwen
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added a notebook on how to read an image
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‎.gitignore

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# emacs
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.#*
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# Jupyter
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.ipynb_checkpoints
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# Byte-compiled / optimized / DLL files
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__pycache__/
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*.py[cod]

‎README.md

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@@ -5,13 +5,12 @@ In this repository you will find a couple of examples on how to use SimpleITK wi
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An open source viewer (and more) for medical images is [Slicer 3D](http://slicer.org).
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Current:
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Currently we have the Python examples:
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- *dcm_to_nrrd.py*: reads a complete dicom series from a folder and converts this to a nrrd file. Reads the window and level tags, and crops the image to this.
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- *resample_isotropically.py*: An example to read in a image file, and resample the image to a new grid.
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- *resample_tests.py*: Several ways to downsample an image.
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- *apply_lut.py*: in some DICOM files there is a tag VOILUTFunction. This is an example on how to apply this.
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TODO
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----
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- Juypter notebooks
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- More examples
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Jupyter notebooks:
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- *ReadImage.ipynb*: An example on how to read an image using SimpleITK.

‎data/dot.dcm

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‎notebooks/ReadImage.ipynb

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{
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"cells": [
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{
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"cell_type": "code",
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"execution_count": 1,
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"metadata": {},
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"outputs": [],
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"source": [
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"import SimpleITK as sitk\n",
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"import matplotlib.pyplot as plt\n",
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"%matplotlib inline"
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]
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},
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{
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"cell_type": "markdown",
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"metadata": {},
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"source": [
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"The SimpleITK function ReadImage() can read single file images. Usually SimpleITK can correctly determine the file type from the extension and file itself."
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]
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},
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{
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"cell_type": "code",
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"execution_count": 2,
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"metadata": {},
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"outputs": [],
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"source": [
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"image = sitk.ReadImage('../data/dot.dcm')"
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]
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},
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{
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"cell_type": "markdown",
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"metadata": {},
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"source": [
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"The type of `image` is an instance of the SimpleITK image. We can request several of its properties. Try tab completion or `help(image)` for more examples."
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]
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},
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{
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"cell_type": "code",
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"execution_count": 5,
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"metadata": {},
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"outputs": [
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{
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"data": {
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"text/plain": [
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"(21, 21, 1)"
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]
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},
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"execution_count": 5,
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"metadata": {},
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"output_type": "execute_result"
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}
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],
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"source": [
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"image.GetSize()"
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]
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},
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{
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"cell_type": "markdown",
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"metadata": {},
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"source": [
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"SimpleITK is a 'simple' procedural wrapper around ITK which can be called from Python and other scripting languages. SimpleITK provides a convenient interface to numpy arrays."
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]
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},
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{
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"cell_type": "code",
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"execution_count": 8,
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"metadata": {},
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"outputs": [
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{
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"name": "stdout",
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"output_type": "stream",
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"text": [
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"`array` has type: <type 'numpy.ndarray'>\n",
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"(1, 21, 21, 3)\n"
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]
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}
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],
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"source": [
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"array = sitk.GetArrayFromImage(image)\n",
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"print('`array` has type: {}'.format(type(array)))"
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]
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},
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{
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"cell_type": "markdown",
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"metadata": {},
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"source": [
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"Let us check out the array size."
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]
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},
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{
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"cell_type": "code",
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"execution_count": 9,
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"metadata": {},
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"outputs": [
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{
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"data": {
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"text/plain": [
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"(1, 21, 21, 3)"
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]
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},
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"execution_count": 9,
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"metadata": {},
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"output_type": "execute_result"
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}
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],
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"source": [
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"array.shape"
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]
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},
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{
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"cell_type": "markdown",
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"metadata": {},
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"source": [
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"The first axis is the number of slices, in this case 1. Many medical image formats are 3D and would have the number of slices on the first axis. The last axis is the number of channels. The image `dot.dcm` is an artificial example of an RGB image saved in the DICOM standard."
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]
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},
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{
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"cell_type": "code",
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"execution_count": 14,
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"metadata": {},
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"outputs": [
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{
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"data": {
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"text/plain": [
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"array([[255, 255, 255, 255, 255, 255, 255, 255, 255, 255],\n",
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" [255, 255, 255, 255, 255, 255, 255, 255, 255, 0],\n",
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" [255, 255, 255, 255, 255, 255, 0, 0, 0, 0],\n",
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" [255, 255, 255, 255, 255, 0, 0, 0, 0, 0],\n",
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" [255, 255, 255, 255, 0, 0, 0, 0, 0, 0],\n",
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" [255, 255, 255, 0, 0, 0, 0, 0, 0, 0],\n",
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" [255, 255, 0, 0, 0, 0, 0, 0, 0, 0],\n",
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" [255, 255, 0, 0, 0, 0, 0, 0, 0, 0],\n",
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" [255, 255, 0, 0, 0, 0, 0, 0, 0, 0],\n",
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" [255, 0, 0, 0, 0, 0, 0, 0, 0, 0]], dtype=uint8)"
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]
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},
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"execution_count": 14,
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"metadata": {},
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"output_type": "execute_result"
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}
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],
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"source": [
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"array[0, 0:10, 0:10, 1]"
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]
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},
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{
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"cell_type": "markdown",
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"metadata": {},
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"source": [
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"As a `numpy` array we can easily plot the image."
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]
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},
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{
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"cell_type": "code",
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"execution_count": 18,
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"metadata": {},
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"outputs": [
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{
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"data": {
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"image/png": 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161+
"text/plain": [
162+
"<matplotlib.figure.Figure at 0x7ff2a50f1c10>"
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]
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},
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"metadata": {},
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"output_type": "display_data"
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}
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],
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"source": [
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"fig = plt.figure(figsize=(6, 6))\n",
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"plt.imshow(array[0]);"
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]
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},
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{
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"cell_type": "code",
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"execution_count": null,
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"metadata": {
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"collapsed": true
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},
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"outputs": [],
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"source": []
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}
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],
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"metadata": {
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"kernelspec": {
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"display_name": "Python 2",
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"language": "python",
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"name": "python2"
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},
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"language_info": {
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"codemirror_mode": {
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"name": "ipython",
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"version": 2
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},
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"file_extension": ".py",
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"mimetype": "text/x-python",
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"name": "python",
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"nbconvert_exporter": "python",
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"pygments_lexer": "ipython2",
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"version": "2.7.12+"
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}
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},
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"nbformat": 4,
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"nbformat_minor": 2
205+
}

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