7.2.5. algotom.prep.removal¶
Module of removal methods in the preprocessing stage:
Many methods for removing stripe artifact in a sinogram (<-> ring artifact in a reconstructed image).
A zinger removal method.
Blob removal methods.
Functions:
|
Remove stripe artifacts in a sinogram using the sorting technique, algorithm 3 in Ref. |
|
Remove stripe artifacts in a sinogram using the filtering technique, algorithm 2 in Ref. |
|
Remove stripe artifacts in a sinogram using the fitting technique, algorithm 1 in Ref. |
|
Remove large stripe artifacts in a sinogram, algorithm 5 in Ref. |
|
Remove unresponsive or fluctuating stripe artifacts in a sinogram, algorithm 6 in Ref. |
|
Remove all types of stripe artifacts in a sinogram by combining algorithm 6, 5, 4, and 3 in Ref. |
Remove stripes using a 2D low-pass filter and the sorting-based technique, algorithm in section 3.3.4 in Ref. |
|
|
Remove stripes using the method in Ref. |
|
Remove stripes using the method in Ref. |
|
Remove stripes using the method in Ref. |
|
Remove stripes using the method in Ref. |
|
Combination of algorithm 4, 5, and 6 in Ref. |
|
Check if the size of a zinger is smaller than a given size. |
|
Select zingers smaller than a certain size. |
|
Remove zinger using the method in Ref. |
|
Generate a binary mask of blobs from a flat-field image (Ref. |
|
Remove blobs in one row of a sinogram, e.g. |
|
Remove blobs in an image. |
- algotom.prep.removal.remove_stripe_based_sorting(sinogram, size=21, dim=1, **options)[source]¶
Remove stripe artifacts in a sinogram using the sorting technique, algorithm 3 in Ref. [1]. Angular direction is along the axis 0.
- Parameters
sinogram (array_like) – 2D array. Sinogram image.
size (int) – Window size of the median filter.
dim ({1, 2}, optional) – Dimension of the window.
options (dict, optional) – Use another smoothing filter rather than the median filter. E.g. options={“method”: “gaussian_filter”, “para1”: (1,21)}
- Returns
array_like – 2D array. Stripe-removed sinogram.
References
- algotom.prep.removal.remove_stripe_based_filtering(sinogram, sigma=3, size=21, dim=1, sort=True, **options)[source]¶
Remove stripe artifacts in a sinogram using the filtering technique, algorithm 2 in Ref. [1]. Angular direction is along the axis 0.
- Parameters
sinogram (array_like) – 2D array. Sinogram image
sigma (int) – Sigma of the Gaussian window used to separate the low-pass and high-pass components of the intensity profile of each column.
size (int) – Window size of the median filter.
dim ({1, 2}, optional) – Dimension of the window.
sort (bool, optional) – Apply sorting if True.
options (dict, optional) – Use another smoothing filter rather than the median filter. E.g. options={“method”: “gaussian_filter”, “para1”: (1,21))}.
- Returns
array_like – 2D array. Stripe-removed sinogram.
References
- algotom.prep.removal.remove_stripe_based_fitting(sinogram, order=2, sigma=10, sort=False, num_chunk=1, **options)[source]¶
Remove stripe artifacts in a sinogram using the fitting technique, algorithm 1 in Ref. [1]. Angular direction is along the axis 0.
- Parameters
sinogram (array_like) – 2D array. Sinogram image
order (int) – Polynomial fit order.
sigma (int) – Sigma of the Gaussian window in the x-direction. Smaller is stronger.
sort (bool, optional) – Apply sorting if True.
num_chunk (int) – Number of chunks of rows to apply the fitting.
options (dict, optional) – Use another smoothing filter rather than the Fourier gaussian filter. E.g. options={“method”: “gaussian_filter”, “para1”: (1,21))}.
- Returns
array_like – 2D array. Stripe-removed sinogram.
References
- algotom.prep.removal.remove_large_stripe(sinogram, snr=3.0, size=51, drop_ratio=0.1, norm=True, **options)[source]¶
Remove large stripe artifacts in a sinogram, algorithm 5 in Ref. [1]. Angular direction is along the axis 0.
- Parameters
sinogram (array_like) – 2D array. Sinogram image
snr (float) – Ratio (>1.0) for stripe detection. Greater is less sensitive.
size (int) – Window size of the median filter.
drop_ratio (float, optional) – Ratio of pixels to be dropped, which is used to reduce the possibility of the false detection of stripes.
norm (bool, optional) – Apply normalization if True.
options (dict, optional) – Use another smoothing filter rather than the median filter. E.g. options={“method”: “gaussian_filter”, “para1”: (1,21))}.
- Returns
array_like – 2D array. Stripe-removed sinogram.
References
- algotom.prep.removal.remove_dead_stripe(sinogram, snr=3.0, size=51, residual=True, smooth_strength=10)[source]¶
Remove unresponsive or fluctuating stripe artifacts in a sinogram, algorithm 6 in Ref. [1]. Angular direction is along the axis 0.
- Parameters
sinogram (array_like) – 2D array. Sinogram image.
snr (float) – Ratio (>1.0) for stripe detection. Greater is less sensitive.
size (int) – Window size of the median filter.
residual (bool, optional) – Removing residual stripes if True.
smooth_strength (int, optional) – Window size of the uniform filter used to detect stripes.
- Returns
ndarray – 2D array. Stripe-removed sinogram.
References
- algotom.prep.removal.remove_all_stripe(sinogram, snr=3.0, la_size=51, sm_size=21, drop_ratio=0.1, dim=1, **options)[source]¶
Remove all types of stripe artifacts in a sinogram by combining algorithm 6, 5, 4, and 3 in Ref. [1]. Angular direction is along the axis 0.
- Parameters
sinogram (array_like) – 2D array. Sinogram image.
snr (float) – Ratio (>1.0) for stripe detection. Greater is less sensitive.
la_size (int) – Window size of the median filter to remove large stripes.
sm_size (int) – Window size of the median filter to remove small-to-medium stripes.
drop_ratio (float, optional) – Ratio of pixels to be dropped, which is used to reduce the possibility of the false detection of stripes.
dim ({1, 2}, optional) – Dimension of the window.
options (dict, optional) – Use another smoothing filter rather than the median filter. E.g. options={“method”: “gaussian_filter”, “para1”: (1,21))}
- Returns
array_like – 2D array. Stripe-removed sinogram.
References
- algotom.prep.removal.remove_stripe_based_2d_filtering_sorting(sinogram, sigma=3, size=21, dim=1, **options)[source]¶
Remove stripes using a 2D low-pass filter and the sorting-based technique, algorithm in section 3.3.4 in Ref. [1]. Angular direction is along the axis 0.
- Parameters
sinogram (array_like) – 2D array. Sinogram image.
sigma (int) – Sigma of the Gaussian window.
size (int) – Window size of the median filter.
dim ({1, 2}, optional) – Dimension of the window.
- Returns
array_like – 2D array. Stripe-removed sinogram.
References
- algotom.prep.removal.remove_stripe_based_normalization(sinogram, sigma=15, num_chunk=1, sort=True, **options)[source]¶
Remove stripes using the method in Ref. [1]. Angular direction is along the axis 0.
- Parameters
sinogram (array_like) – 2D array. Sinogram image.
sigma (int) – Sigma of the Gaussian window.
num_chunk (int) – Number of chunks of rows.
sort (bool, optional) – Apply sorting (Ref. [2]) if True.
options (dict, optional) – Use another smoothing 1D-filter rather than the Gaussian filter. E.g. options={“method”: “median_filter”, “para1”: 21)}.
- Returns
array_like – 2D array. Stripe-removed sinogram.
References
[1] : https://www.mcs.anl.gov/research/projects/X-ray-cmt/rivers/tutorial.html
- algotom.prep.removal.remove_stripe_based_regularization(sinogram, alpha=0.0005, num_chunk=1, apply_log=True, sort=True)[source]¶
Remove stripes using the method in Ref. [1]. Angular direction is along the axis 0.
- Parameters
sinogram (array_like) – 2D array. Sinogram image.
alpha (float) – Regularization parameter, e.g. 0.0005. Smaller is stronger.
num_chunk (int) – Number of chunks of rows.
apply_log (bool) – Apply the logarithm function to the sinogram if True.
sort (bool, optional) – Apply sorting (Ref. [2]) if True.
- Returns
array_like – 2D array. Stripe-removed sinogram.
References
- algotom.prep.removal.remove_stripe_based_fft(sinogram, u=20, n=8, v=1, sort=False)[source]¶
Remove stripes using the method in Ref. [1]. Angular direction is along the axis 0.
- Parameters
sinogram (array_like) – 2D array. Sinogram image.
u (int) – Cutoff frequency.
n (int) – Filter order.
v (int) – Number of rows (* 2) to be applied the filter.
sort (bool, optional) – Apply sorting (Ref. [2]) if True.
- Returns
ndarray – 2D array. Stripe-removed sinogram.
References
- algotom.prep.removal.remove_stripe_based_wavelet_fft(sinogram, level=5, size=1, wavelet_name='db9', window_name='gaussian', sort=False, **options)[source]¶
Remove stripes using the method in Ref. [1]. Angular direction is along the axis 0.
- Parameters
sinogram (array_like) – 2D array. Sinogram image.
level (int) – Wavelet decomposition level.
size (int) – Damping parameter. Larger is stronger.
wavelet_name (str) – Name of a wavelet. Search pywavelets API for a full list.
window_name (str) – High-pass window. Two options: “gaussian” or “butter”.
sort (bool, optional) – Apply sorting (Ref. [2]) if True.
options (dict, optional) – Use another smoothing filter rather than the fft-gaussian-filter. E.g. options={“method”: “gaussian_filter”, “para1”: (1,11))}
- Returns
array_like – 2D array. Stripe-removed sinogram.
References
- algotom.prep.removal.remove_stripe_based_interpolation(sinogram, snr=3.0, size=51, drop_ratio=0.1, norm=True, kind='linear', **options)[source]¶
Combination of algorithm 4, 5, and 6 in Ref. [1]. Angular direction is along the axis 0.
- Parameters
sinogram (array_like) – 2D array. Sinogram image
snr (float) – Ratio (>1.0) for stripe detection. Greater is less sensitive.
size (int) – Window size of the median filter used to detect stripes.
drop_ratio (float, optional) – Ratio of pixels to be dropped, which is used to reduce the possibility of the false detection of stripes.
norm (bool, optional) – Apply normalization if True.
kind ({‘linear’, ‘cubic’, ‘quintic’}, optional) – The kind of spline interpolation to use. Default is ‘linear’.
options (dict, optional) – Use another smoothing filter rather than the median filter. E.g. options={“method”: “gaussian_filter”, “para1”: (1,21))}
- Returns
array_like – 2D array. Stripe-removed sinogram.
References
- algotom.prep.removal.check_zinger_size(mat, max_size)[source]¶
Check if the size of a zinger is smaller than a given size.
- Parameters
mat (array_like) – 2D array.
max_size (int) – Maximum size.
- Returns
bool
- algotom.prep.removal.select_zinger(mat, max_size)[source]¶
Select zingers smaller than a certain size.
- Parameters
mat (array_like) – 2D array.
max_size (int) – Maximum size in pixel.
- Returns
array_like – 2D binary array.
- algotom.prep.removal.remove_zinger(mat, threshold, size=2, check_size=False)[source]¶
Remove zinger using the method in Ref. [1], working on a projection image or sinogram image.
- Parameters
mat (array_like) – 2D array. Projection image or sinogram image.
threshold (float) – Threshold to segment zingers. Smaller is more sensitive. Recommended range [0.05, 0.1].
size (int) – Size of a zinger.
check_size (bool) – Enable/disable size checking before removal.
- Returns
array_like – 2D array. Zinger-removed image.
References
- algotom.prep.removal.generate_blob_mask(flat, size, snr)[source]¶
Generate a binary mask of blobs from a flat-field image (Ref. [1]).
- Parameters
flat (array_like) – 2D array. Flat-field image.
size (float) – Estimated size of the largest blob.
snr (float) – Ratio used to segment blobs.
- Returns
array_like – 2D array. Binary mask.
References
- algotom.prep.removal.remove_blob_1d(sino_1d, mask_1d)[source]¶
Remove blobs in one row of a sinogram, e.g. for a helical scan as shown in Ref. [1].
- Parameters
sino_1d (array_like) – 1D array. A row of a sinogram.
mask_1d (array_like) – 1D binary mask.
- Returns
array_like – 1D array.
Notes
The method is used to remove streak artifacts caused by blobs in a sinogram generated from a helical-scan data [1].
References