Reading *.mhd/*.raw Format In Python

Can anyone please tell me the way I can read a dataset containing .mhd/.raw files in python?

Solution 1:

The easiest way is to use SimpleITK (MedPy uses ITK for .mhd/.raw files too). Command

pip install SimpleITK

works for many python versions. For reading .mhd/.raw you can use this code from kaggle

import SimpleITK as sitk
import numpy as np
'''
This funciton reads a '.mhd' file using SimpleITK and return the image array, origin and spacing of the image.
'''

def load_itk(filename):
    # Reads the image using SimpleITK
    itkimage = sitk.ReadImage(filename)

    # Convert the image to a  numpy array first and then shuffle the dimensions to get axis in the order z,y,x
    ct_scan = sitk.GetArrayFromImage(itkimage)

    # Read the origin of the ct_scan, will be used to convert the coordinates from world to voxel and vice versa.
    origin = np.array(list(reversed(itkimage.GetOrigin())))

    # Read the spacing along each dimension
    spacing = np.array(list(reversed(itkimage.GetSpacing())))

    return ct_scan, origin, spacing

Solution 2:

Using skimage may be even easier after you installed SimpleITK

import skimage.io as io
img = io.imread('file.mhd', plugin='simpleitk')

This will give you a numpy array with z,y,x sorting.

Solution 3:

Adding on the above posts, you can start with a CT-Scan .mhd file downloaded from the here and display / save 29 images with the following code (assuming that you have both the header and the raw files downloaded in the current directory):

import SimpleITK as sitk
import matplotlib.pylab as plt
ct_scans = sitk.GetArrayFromImage(sitk.ReadImage("training_001_ct.mhd", sitk.sitkFloat32))
plt.figure(figsize=(20,16))
plt.gray()
plt.subplots_adjust(0,0,1,1,0.01,0.01)
for i in range(ct_scans.shape[0]):
    plt.subplot(5,6,i+1), plt.imshow(ct_scans[i]), plt.axis('off')
    # use plt.savefig(...) here if you want to save the images as .jpg, e.g.,
plt.show()

Here is the same CT-scan .mhd file that is read with SimpleITK and animated:

Solution 4:

You can try to use MedPy or this mhd_utils script