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Display DICOM using pydicom

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Merry Christmas everybody. Let's take a look at the Christmas Lights around Lafarge Lake in Coquitlam FIRST.

Lafarge Lake Christmas Lights 01 Lafarge Lake Christmas Lights 02
Lafarge Lake Christmas Lights 01 Lafarge Lake Christmas Lights 02
Lafarge Lake Christmas Lights 03 Lafarge Lake Christmas Lights 04
Lafarge Lake Christmas Lights 03 Lafarge Lake Christmas Lights 04
Lafarge Lake Christmas Lights 05 Lafarge Lake Christmas Lights 06
Lafarge Lake Christmas Lights 05 Lafarge Lake Christmas Lights 06

Now, it's the time to test PyDicom.

Get Started

Copy and paste that piece of code from Medical Image Analysis with Deep Learning  with trivial modifications as follows:

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import cv2
import numpy as np
import matplotlib.pyplot as plt
import pydicom as pdicom
import os
import glob
import pandas as pd
import scipy.ndimage
from skimage import measure, morphology
from mpl_toolkits.mplot3d.art3d import Poly3DCollection


# Prepare dicom images
INPUT_FOLDER = './'
patients = os.listdir(INPUT_FOLDER)
patients.sort()


# Collect all dicom images
lstFilesDCM = []   # create an empty list
def load_scan2(path):
    for dirName, subdirList, fileList in os.walk(path):
        for filename in fileList:
            if ".dcm" in filename.lower():  
                lstFilesDCM.append(os.path.join(dirName, filename))
    return lstFilesDCM

first_patient = load_scan2(INPUT_FOLDER)


# Get ref file
print (lstFilesDCM[0])
RefDs = pdicom.read_file(lstFilesDCM[0])

# Load dimensions based on the number of rows, columns and slices (along 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))


x = np.arange(0.0, (ConstPixelDims[0]+1)*ConstPixelSpacing[0], ConstPixelSpacing[0])
y = np.arange(0.0, (ConstPixelDims[1]+1)*ConstPixelSpacing[1], ConstPixelSpacing[1])
z = np.arange(0.0, (ConstPixelDims[2]+1)*ConstPixelSpacing[2], ConstPixelSpacing[2])


# The array is sized based on 'ConstPixelDims'

ArrayDicom = np.zeros(ConstPixelDims, dtype=RefDs.pixel_array.dtype)

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


plt.figure(dpi=1600)
plt.axes().set_aspect('equal', 'datalim')
plt.set_cmap(plt.gray())
plt.pcolormesh(y, x, np.flipud(ArrayDicom[:, :, 80]))
plt.show()

Display DICOM

I did a MRI scan in 2015, in SIAT, for the purpose of research. The scan resolution is NOT very high. Anyway, a bunch of DICOM images can be viewed as follows:

DICOM images
MRI - me