Load CT slices and plot axial, sagittal and coronal imagesΒΆ

This example illustrates loading multiple files, sorting them by slice location, building a 3D image and reslicing it in different planes.

import pydicom
import numpy as np
import matplotlib.pyplot as plt
import sys
import glob

# load the DICOM files
files = []
print('glob: {}'.format(sys.argv[1]))
for fname in glob.glob(sys.argv[1], recursive=False):
    print("loading: {}".format(fname))

print("file count: {}".format(len(files)))

# skip files with no SliceLocation (eg scout views)
slices = []
skipcount = 0
for f in files:
    if hasattr(f, 'SliceLocation'):
        skipcount = skipcount + 1

print("skipped, no SliceLocation: {}".format(skipcount))

# ensure they are in the correct order
slices = sorted(slices, key=lambda s: s.SliceLocation)

# pixel aspects, assuming all slices are the same
ps = slices[0].PixelSpacing
ss = slices[0].SliceThickness
ax_aspect = ps[1]/ps[0]
sag_aspect = ps[1]/ss
cor_aspect = ss/ps[0]

# create 3D array
img_shape = list(slices[0].pixel_array.shape)
img3d = np.zeros(img_shape)

# fill 3D array with the images from the files
for i, s in enumerate(slices):
    img2d = s.pixel_array
    img3d[:, :, i] = img2d

# plot 3 orthogonal slices
a1 = plt.subplot(2, 2, 1)
plt.imshow(img3d[:, :, img_shape[2]//2])

a2 = plt.subplot(2, 2, 2)
plt.imshow(img3d[:, img_shape[1]//2, :])

a3 = plt.subplot(2, 2, 3)
plt.imshow(img3d[img_shape[0]//2, :, :].T)


Total running time of the script: ( 0 minutes 0.000 seconds)

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