An image before and after windowed()

Let’s visualize the image before and after applying the windowed() function. In the code below, we can see the difference between the original image and the image after applying windowed().

Python 3.8

from matplotlib import pyplot as plt 
import nibabel
import numpy as np
filepath = 'volume_pt5/volume-44.nii' 
imagedata = nibabel.load(filepath) 
array = imagedata.get_fdata()
array = np.rot90(np.array(array))
def windowed(px, w, l):
        px_min = l - w//2
        px_max = l + w//2
        px[px<px_min] = px_min
        px[px>px_max] = px_max
        return (px-px_min) / (px_max-px_min)
f = plt.figure(figsize=(12,12)) 
ax = f.add_subplot(121)
ax2 = f.add_subplot(122)
ax.imshow(array[...,50].astype(np.float32),cmap=plt.cm.bone) 
ax.title.set_text('Original image') 
ax2.imshow(windowed(array[...,50].astype(np.float32), 150,30), cmap=plt.cm.bone)
ax2.title.set_text('Windowing image')

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Visualize the Windowing

An image before and after windowed()