How to access RGB pixel arrays from DICOM files using pydicom?

Question

I try to access a DICOM file's RGB pixel array with unknown compression (maybe none). Extracting grayscale pixel arrays works completely fine.

However, using

import dicom
import numpy as np

data_set = dicom.read_file(path)
pixel_array = data_set.pixel_array
size_of_array = pixel_array.shape

if len(size_of_array ) == 3:     
    chanR = pixel_array[0][0:size_of_array[1], 0:size_of_array[2]]
    chanG = pixel_array[1][0:size_of_array[1], 0:size_of_array[2]]
    chanB = pixel_array[2][0:size_of_array[1], 0:size_of_array[2]]
    output_array = (0.299 ** chanR) + (0.587 ** chanG) + (0.114 ** chanB)

with the goal to convert it to an common grayscale array. Unfortunately the result array output_array is not containing correct pixel data. Contents are not false scaled, they are spatially disturbed. Where is the issue?

Answer 1

It is not RGB pixel array and the better way is converting to gray image.

The way to get CT Image is to get the attribute of pixel_array in CT dicom file. The type of elements in pixel_array of CT dicom file are all uint16.But a lot of tool in python, like OpenCV, Some AI stuff, cannot be compatible with the type.

After getting pixel_array (CT Image) from CT dicom file, you always need to convert the pixel_array into gray image, so that you can process this gray image by a lot of image processing tool in python.

The following code is a working example to convert pixel_array into gray image.

import matplotlib.pyplot as plt
import os
import pydicom
import numpy as np 
# Abvoe code is to import dependent libraries of this code

# Read some CT dicom file here by pydicom library
ct_filepath = r"<YOUR_CT_DICOM_FILEPATH>"
ct_dicom = pydicom.read_file(ct_filepath)
img = ct_dicom.pixel_array

# Now, img is pixel_array. it is input of our demo code

# Convert pixel_array (img) to -> gray image (img_2d_scaled)
## Step 1. Convert to float to avoid overflow or underflow losses.
img_2d = img.astype(float)

## Step 2. Rescaling grey scale between 0-255
img_2d_scaled = (np.maximum(img_2d,0) / img_2d.max()) * 255.0

## Step 3. Convert to uint
img_2d_scaled = np.uint8(img_2d_scaled)


# Show information of input and output in above code
## (1) Show information of original CT image 
print(img.dtype)
print(img.shape)
print(img)

## (2) Show information of gray image of it 
print(img_2d_scaled.dtype)
print(img_2d_scaled.shape)
print(img_2d_scaled)

## (3) Show the scaled gray image by matplotlib
plt.imshow(img_2d_scaled, cmap='gray', vmin=0, vmax=255)
plt.show()

And the following is result of what I print out.