Visit the wiki pages to find some additional documentation and instructions on how view an interactive verson of these notebooks using binder.
In [1]:
%matplotlib inline
In [2]:
import pydicom

import os
import numpy
import matplotlib.pyplot as plt

Reading a DICOM image series using pydicom

Here we are using the pydicom library!. Make sure you have this library installed as part of your local python installation. Otherwise run the following command:

$ conda install -c conda-forge pydicom

This example has been adopted from DICOM in Python! Tutorial.

In [3]:
from pydicom.filereader import read_dicomdir
In [4]:
base_dir = '../images/Marching_Man/'
In [5]:
PathDicom = '../images/Marching_Man/'
lstFilesDCM = []  # create an empty list
for dirName, subdirList, fileList in os.walk(PathDicom):
    for filename in fileList:
        if ".dcm" in filename.lower():  # check whether the file's DICOM
            lstFilesDCM.append(os.path.join(dirName,filename))

Now, lets get into the pydicom part of the code. A notable aspect of this package is that upon reading a DICOM file, it creates a dicom.dataset.FileDataset object where the different metadata are assigned to object attributes with the same name. We’ll see this below:

In [6]:
# Get ref file
RefDs = pydicom.read_file(lstFilesDCM[0])

# Load dimensions based on the number of rows, columns, and slices (along the Z axis)
ConstPixelDims = (int(RefDs.Rows), int(RefDs.Columns), len(lstFilesDCM))

# Load spacing values (in mm)
ConstPixelSpacing = (float(RefDs.PixelSpacing[0]), float(RefDs.PixelSpacing[1]), float(RefDs.SliceThickness))

We simply use numpy.arange, ConstPixelDims, and ConstPixelSpacing to calculate axes for this array. Next, comes the last pydicom part:

In [7]:
x = numpy.arange(0.0, (ConstPixelDims[0]+1)*ConstPixelSpacing[0], ConstPixelSpacing[0])
y = numpy.arange(0.0, (ConstPixelDims[1]+1)*ConstPixelSpacing[1], ConstPixelSpacing[1])
z = numpy.arange(0.0, (ConstPixelDims[2]+1)*ConstPixelSpacing[2], ConstPixelSpacing[2])
In [8]:
# The array is sized based on 'ConstPixelDims'
ArrayDicom = numpy.zeros(ConstPixelDims, dtype=RefDs.pixel_array.dtype)

# loop through all the DICOM files
for filenameDCM in lstFilesDCM:
    # read the file
    ds = pydicom.read_file(filenameDCM)
    # store the raw image data
    ArrayDicom[:, :, lstFilesDCM.index(filenameDCM)] = ds.pixel_array  
    
ArrayDicom.shape
Out[8]:
(256, 256, 93)

Now you can select the which slice should be plotted. As you can see the dataset has 93 slices

In [9]:
plt.figure(dpi=300)
plt.axes().set_aspect('equal', 'datalim')
plt.set_cmap(plt.gray())
plt.pcolormesh(x, y, numpy.flipud(ArrayDicom[:, :, 92]))
Out[9]:
<matplotlib.collections.QuadMesh at 0x7ffedb4fadd0>

Reading a DICOM image series using simpleITK

You can achive the same task using the simpleITK library!. Make sure you have this library installed as part of your local python installation. Otherwise run the following command:

$ conda install -c simpleitk simpleitk
In [10]:
import SimpleITK as sitk
In [11]:
reader = sitk.ImageSeriesReader()
In [12]:
dicom_names = reader.GetGDCMSeriesFileNames('../images/Marching_Man' )
In [13]:
reader.SetFileNames(dicom_names)
In [14]:
volume = reader.Execute()

Now convert the ITK volume into a numpy array

In [15]:
nda = sitk.GetArrayFromImage(volume)
nda.shape
Out[15]:
(93, 256, 256)
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