Note
Click here to download the full example code
Denoise images using Non-Local Means (NLMEANS)
Using the non-local means filter [Coupe08] and [Coupe11] and you can denoise 3D or 4D images and boost the SNR of your datasets. You can also decide between modeling the noise as Gaussian or Rician (default).
import numpy as np
import matplotlib.pyplot as plt
from time import time
from dipy.denoise.nlmeans import nlmeans
from dipy.denoise.noise_estimate import estimate_sigma
from dipy.data import get_fnames
from dipy.io.image import load_nifti, save_nifti
t1_fname = get_fnames('stanford_t1')
data, affine = load_nifti(t1_fname)
mask = data > 1500
print("vol size", data.shape)
vol size (81, 106, 76)
In order to call non_local_means first you need to estimate the standard
deviation of the noise. We have used N=32 since the Stanford dataset was
acquired on a 3T GE scanner with a 32 array head coil.
sigma = estimate_sigma(data, N=32)
Calling the main function non_local_means
t = time()
den = nlmeans(data, sigma=sigma, mask=mask, patch_radius=1,
block_radius=2, rician=True)
print("total time", time() - t)
total time 0.6275289058685303
Let us plot the axial slice of the denoised output
axial_middle = data.shape[2] // 2
before = data[:, :, axial_middle].T
after = den[:, :, axial_middle].T
difference = np.abs(after.astype(np.float64) - before.astype(np.float64))
difference[~mask[:, :, axial_middle].T] = 0
fig, ax = plt.subplots(1, 3)
ax[0].imshow(before, cmap='gray', origin='lower')
ax[0].set_title('before')
ax[1].imshow(after, cmap='gray', origin='lower')
ax[1].set_title('after')
ax[2].imshow(difference, cmap='gray', origin='lower')
ax[2].set_title('difference')
plt.savefig('denoised.png', bbox_inches='tight')
Showing axial slice before (left) and after (right) NLMEANS denoising
save_nifti('denoised.nii.gz', den, affine)
An improved version of non-local means denoising is adaptive soft coefficient matching, please refer to example_denoise_ascm for more details.
References
P. Coupe, P. Yger, S. Prima, P. Hellier, C. Kervrann, C. Barillot, “An Optimized Blockwise Non Local Means Denoising Filter for 3D Magnetic Resonance Images”, IEEE Transactions on Medical Imaging, 27(4):425-441, 2008
Pierrick Coupe, Jose Manjon, Montserrat Robles, Louis Collins. “Adaptive Multiresolution Non-Local Means Filter for 3D MR Image Denoising” IET Image Processing, Institution of Engineering and Technology, 2011
Total running time of the script: ( 0 minutes 0.898 seconds)