Unable to get dicom image for display in python

Question

I'm trying to display a DICOM image in opencv-python.I am using the pydicom library,And then adding API's to create a full fledged DICOM viewer with DOTNET, that runs python(C# calls python with process instance of course!!). I am unable to convert or see the uncompressed DICOM image. whenever i try to load or modify the pixel_array. I get error messges.

import dicom
import cv2
import numpy
df=dicom.read_file("IM-0001-0002.dcm")
df.pixel_array

Traceback (most recent call last):
File "<pyshell#4>", line 1, in <module>
df.pixel_array
File "C:\Python27\lib\site-packages\dicom\dataset.py", line 394, in pixel_array
return self._get_pixel_array()
File "C:\Python27\lib\site-packages\dicom\dataset.py", line 376, in _get_pixel_array
raise NotImplementedError("Pixel Data is compressed in a format pydicom does not yet          handle. Cannot return array")
NotImplementedError: Pixel Data is compressed in a format pydicom does not yet handle.        Cannot return array

Please suggest me good way to convert the image making cv2.imshow() function o display the image

Thanks in Advance!!

Answer 1

Try pydicom

One reason for the error can be: the .dcm file used may contain the format that is not supported (e.g. JPEG 2000 is not supported by pillow in case of pydicom). This issue can be solved. I was having the same issue (I am using pydicom instead of dicom) I guess you will get some direction from the solution that solved my problem:

1st Platforma Information:

I am using: pydicom to read .dcm files, Python 3.6, Anaconda and Ubuntu, 15 GB RAM

Solution:

1. Install pydicom using this command: pip install -U pydicom.

Information can be found here: (link: https://pydicom.github.io/pydicom/dev/getting_started.html)

2. Anaconda is necessary. Why? Please check the official doc of pydicom (https://pydicom.github.io/pydicom/dev/getting_started.html) its mentioned "To install pydicom along with image handlers for compressed pixel data, we encourage you to use Miniconda or Anaconda"

3. If you are using Ubuntu directly open Terminal. If you are using Windows then on Anaconda Navigator go to Environment from here start terminal. Execute the following commands on it:

   pip install -U git+https://github.com/pydicom/pydicom.git

   conda install pydicom --channel conda-forge

   conda install -c conda-forge gdcm

Cross Check:

Now restart the notebook and then try to execute your code using pydicom. It will display the output.

Also, you can use Matplotlib to display as follows:

import matplotlib.pyplot as plt
import pydicom
filename = 'IM-0001-0002.dcm'
ds = pydicom.dcmread(filename)
plt.imshow(ds.pixel_array, cmap=plt.cm.bone)

I hope it will help you.

Answer 2

You can use the python GDCM binding to decompress the file first, see for example here

Answer 3

Since pydicom do not support compressed dicom files, you will have to decompress it first. You can use GDCM to do that.

Answer 4

you have to convert in RGB before, look at that for a monochrome dicom file: https://github.com/twanmal/dicom_monochrome_to_opencv

# import the necessary packages
from imutils import contours
import scipy
from skimage import measure
import numpy as np # numeric library needed
import pandas as pd #for datafrome
import argparse # simple argparser
import imutils
import cv2  # for opencv image recognising tool
import dicom
filename = askopenfilename()
dicom_file = dicom.read_file(filename) ## original dicom File
#### a dicom monochrome-2 file has pixel value between approx -2000 and +2000, opencv doesn't work with it#####
#### in a first step we transform those pixel values in (R,G,B)
### to have gray in RGB, simply give the same values for R,G, and B, 
####(0,0,0) will be black, (255,255,255) will be white,

## the threeshold to be automized with a proper quartile function of the pixel distribution
black_threeshold=0###pixel value below 0 will be black,
white_threeshold=1400###pixel value above 1400 will be white
wt=white_threeshold
bt=black_threeshold

###### function to transform a dicom to RGB for the use of opencv, 
##to be strongly improved, as it takes to much time to run,
## and the linear process should be replaced with an adapted weighted arctan or an adapted spline interpolation.
def DicomtoRGB(dicomfile,bt,wt):
    """Create new image(numpy array) filled with certain color in RGB"""
    # Create black blank image
    image = np.zeros((dicomfile.Rows, dicomfile.Columns, 3), np.uint8)
    #loops on image height and width
    i=0
    j=0
    while i<dicomfile.Rows:
        j=0
        while j<dicomfile.Columns:
            color = yaxpb(dicom_file.pixel_array[i][j],bt,wt) #linear transformation to be adapted
            image[i][j] = (color,color,color)## same R,G, B value to obtain greyscale
            j=j+1
        i=i+1
    return image
##linear transformation : from [bt < pxvalue < wt] linear to [0<pyvalue<255]: loss of information... 
def yaxpb(pxvalue,bt,wt):
    if pxvalue < bt:
        y=0
    elif pxvalue > wt:
        y=255
    else:
        y=pxvalue*255/(wt-bt)-255*bt/(wt-bt)
    return y



image=DicomtoRGB(dicom_file,bt=0,wt=1400)
## loading the RGB in a proper opencv format
gray = cv2.cvtColor(image, cv2.COLOR_BGR2GRAY)
## look at the gray file
cv2.imshow("gray", gray)
cv2.waitKey(0)
cv2.destroyWindow("gray")