python - Overlay an image segmentation with numpy and matplotlib

Question

I am trying to overlay two images. The first one is a 512x512 NumPy array (from a CT image). The second one is also a 512x512 NumPy array but I am just interested in the pixels where the value is larger than 0 (a functional image).

To do that I am trying to create a masked array.

import numpy as np 
import numpy.ma as ma
import matplotlib.pyplot as plt

# Both images are loaded from a dicom. Both are numpy arrays of (512,512) 
Image1 = readimage(path)
Image2 = readimage(path)
# Create image 2 mask
mask = ma.masked_where(Image2>0, Image2)
Image2_mask = ma.masked_array(Image2,mask)

# Plot images
plt.figure(dpi=300)
y, x = np.mgrid[1:513,1:513]
plt.axes().set_aspect('equal', 'datalim')
plt.set_cmap(plt.gray())
plt.pcolormesh(x, y, Image1,cmap='gray')
plt.pcolormesh(x, y, Image2_mask,cmap='jet')
plt.axis([x.min(), x.max(), y.min(), y.max()])
plt.colorbar()
plt.show()

This code does not show any overlay. What I am doing wrong? Is there any straight way? I am coming from a Matlab environment and I am quite new to Python.

Answer 1

Why don't you use imshow instead?

You can plot a 2D image by doing:

plt.imshow(Image1, cmap='gray') # I would add interpolation='none'

Afterwards, you can easily overlay the segmentation by doing:

plt.imshow(Image2_mask, cmap='jet', alpha=0.5) # interpolation='none'

Changing the alpha will change the opacity of the overlay.

Additionaly, why do you create 2 masks? Only one should be enough, you can do:

Image2_mask = ma.masked_array(Image2 > 0, Image2)

---

Practical example:

import numpy as np
mask = np.zeros((10,10))
mask[3:-3, 3:-3] = 1 # white square in black background
im = mask + np.random.randn(10,10) * 0.01 # random image
masked = np.ma.masked_where(mask == 0, mask)

import matplotlib.pyplot as plt
plt.figure()
plt.subplot(1,2,1)
plt.imshow(im, 'gray', interpolation='none')
plt.subplot(1,2,2)
plt.imshow(im, 'gray', interpolation='none')
plt.imshow(masked, 'jet', interpolation='none', alpha=0.7)
plt.show()