Python opencv and dicom files

Question

while trying to use opencv with a dicom monochrome file, i saw only one solution : first transform the monochrom dicom file with pixel values between -2000( black) to 2000 (white)in RGB with 0<=R=G=B<=255. (To ensure grayscale, i have to set R=G=B) So i made a linear interpolation to go from first [-2000;2000] to [0, 255]. The results for my pictures were not good so i decided to put a black threeshlod under which all pixels are black and a white threeshol above which all pixels are white. Doing so, i could work with opencv but 1) I would like to automize the black thressholds and the white threesholds 2) since i have 512*512 pixels, the double for loop takes time to execute.

Do you have any idea how i could automize and speedup the process ? Or simply a good idea ? the code is :

    # import the necessary packages
from imutils import contours
import matplotlib.pyplot as plt
import matplotlib.mlab as mlab
import scipy
from skimage import measure
import numpy as np # numeric library needed
import pandas as pd #for dataframe
import argparse # simple argparser
import imutils
import cv2  # for opencv image recognising tool
import dicom
from tkinter import Tk
from tkinter.filedialog import askopenfilename
import pdb

#filename = askopenfilename() # show an "Open" dialog box and return the path to the selected file
#filename ="../inputs/12e0e2036f61c8a52ee4471bf813c36a/7e74cdbac4c6db70bade75225258119d.dcm"
dicom_file = dicom.read_file(filename) ## original dicom File
#### a dicom monochrome 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 function.
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)
>>image
array([[[0, 0, 0],
        [0, 0, 0],
        [0, 0, 0],
        ..., 
        [0, 0, 0],
        [0, 0, 0],
        [0, 0, 0]],

       [[0, 0, 0],
        [0, 0, 0],
        [0, 0, 0],
        ..., 
        [0, 0, 0],
        [0, 0, 0],
        [0, 0, 0]],

       [[0, 0, 0],
        [0, 0, 0],
        [0, 0, 0],
        ..., 
        [0, 0, 0],
        [0, 0, 0],
        [0, 0, 0]],

       ..., 
       [[0, 0, 0],
        [0, 0, 0],
        [0, 0, 0],
        ..., 
        [0, 0, 0],
        [0, 0, 0],
        [0, 0, 0]],

       [[0, 0, 0],
        [0, 0, 0],
        [0, 0, 0],
        ..., 
        [0, 0, 0],
        [0, 0, 0],
        [0, 0, 0]],

       [[0, 0, 0],
        [0, 0, 0],
        [0, 0, 0],
        ..., 
        [0, 0, 0],
        [0, 0, 0],
        [0, 0, 0]]], dtype=uint8)

## 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")

Answer 1

EDIT2 - Now performing the proper transformation

---

We can vectorize your entire code with numpy . Here's an example:

import numpy as np

def dicom_to_rgb(img,bt,wt):

    # enforce boundary conditions
    img = np.clip(img,bt,wt)

    # linear transformation
    # multiplicative
    img = np.multiply(img,-255/(wt-bt)).astype(np.int)
    # additive
    img += 255

    # stack thrice on the last axis for R G B
    rgb_img = np.stack([img]*3,axis=-1)

    return rgb_img


    pixels = 512
    img = np.random.randint(-2000,2000,pixels**2).reshape(pixels,pixels)
    bt = 0
    wt = 1400

    rgb = dicom_to_rgb(img,bt,wt)

Or with your input:

dicom_file = dicom.read_file(filename)
img = np.array(dicom_file.pixel_array)

rgb = dicom_to_rgb(img,wt,bt)

Answer 2

I think your problem come with this :

... the double for loop takes time to execute.

You can use remap function from opencv: See this example :