7.2.1. algotom.prep.calculation

Module of calculation methods in the preprocessing stage:

  • Calculating the center-of-rotation (COR) using a 180-degree sinogram.

  • Determining the overlap-side and overlap-area between images.

  • Calculating the COR in a half-acquisition scan (360-degree scan with offset COR).

  • Using the similar technique as above to calculate the COR in a 180-degree scan from two projections.

  • Determining the relative translations between images using phase-correlation technique.

  • Calculating the COR using phase-correlation technique.

Functions:

make_inverse_double_wedge_mask(height, ...)

Generate a double-wedge binary mask using Eq.

calculate_center_metric(center, sino_180, ...)

Calculate a metric of an estimated center-of-rotation.

coarse_search_cor(sino_180, start, stop[, ...])

Find the center-of-rotation (COR) using integer shifting.

fine_search_cor(sino_180, start, radius, step)

Find the center-of-rotation (COR) using sub-pixel shifting.

downsample_cor(image, dsp_fact0, dsp_fact1)

Downsample an image by averaging.

find_center_vo(sino_180[, start, stop, ...])

Find the center-of-rotation using the method described in Ref.

calculate_curvature(list_metric)

Calculate the curvature of a fitted curve going through the minimum value of a metric list.

correlation_metric(mat1, mat2)

Calculate the correlation metric.

search_overlap(mat1, mat2, win_width, side)

Calculate the correlation metrics between a rectangular region, defined by the window width, on the utmost left/right side of image 2 and the same size region in image 1 where the region is slided across image 1.

find_overlap(mat1, mat2, win_width[, side, ...])

Find the overlap area and overlap side between two images (Ref.

find_overlap_multiple(list_mat, win_width[, ...])

Find the overlap-areas and overlap-sides of a list of images where the overlap side referring to the previous image.

find_center_360(sino_360, win_width[, side, ...])

Find the center-of-rotation (COR) in a 360-degree scan with offset COR use the method presented in Ref.

complex_gradient(mat)

Return complex gradient of a 2D array.

find_shift_based_phase_correlation(mat1, mat2)

Find relative translation in x and y direction between images with haft-pixel accuracy (Ref.

find_center_based_phase_correlation(mat1, mat2)

Find the center-of-rotation (COR) using projection images at 0-degree and 180-degree.

find_center_projection(mat1, mat2[, flip, ...])

Find the center-of-rotation (COR) using projection images at 0-degree and 180-degree based on a method in Ref.

calculate_reconstructable_height(y_start, ...)

Calculate reconstructable height in a helical scan.

calculate_maximum_index(y_start, y_stop, ...)

Calculate the maximum index of a reconstructable slice in a helical scan.
algotom.prep.calculation.make_inverse_double_wedge_mask(height, width, radius, hor_drop=None, ver_drop=None)[source]

Generate a double-wedge binary mask using Eq. (3) in Ref. [1]. Values outside the double-wedge region correspond to 1.0.

Parameters

  • height (int) – Image height.

  • width (int) – Image width.

  • radius (int) – Radius of an object, in pixel unit.

  • hor_drop (int or None, optional) – Number of rows (2 * hor_drop) around the middle of the mask with values set to zeros.

  • ver_drop (int or None, optional) – Number of columns (2 * ver_drop) around the middle of the mask with values set to zeros.

Returns

array_like – 2D binary mask.

References

[1] : https://doi.org/10.1364/OE.22.019078

algotom.prep.calculation.calculate_center_metric(center, sino_180, sino_flip, sino_comp, mask)[source]

Calculate a metric of an estimated center-of-rotation.

Parameters

  • center (float) – Estimated center.

  • sino_180 (array_like) – 2D array. 180-degree sinogram.

  • sino_flip (array_like) – 2D array. Flip the 180-degree sinogram in the left/right direction.

  • sino_comp (array_like) – 2D array. Used to fill the gap left by image shifting.

  • mask (array_like) – 2D array. Used to select coefficients in the double-wedge region.

Returns

float – Metric.

algotom.prep.calculation.coarse_search_cor(sino_180, start, stop, ratio=0.5, denoise=True, ncore=None, hor_drop=None, ver_drop=None)[source]

Find the center-of-rotation (COR) using integer shifting.

Parameters

  • sino_180 (array_like) – 2D array. 180-degree sinogram.

  • start (int) – Starting point for searching COR.

  • stop (int) – Ending point for searching COR.

  • ratio (float) – Ratio between a sample and the width of the sinogram.

  • denoise (bool, optional) – Apply a smoothing filter.

  • ncore (int or None) – Number of cpu-cores used for computing. Automatically selected if None.

  • hor_drop (int or None, optional) – Refer the method of “make_inverse_double_wedge_mask”

  • ver_drop (int or None, optional) – Refer the method of “make_inverse_double_wedge_mask”

Returns

float – Center of rotation.

algotom.prep.calculation.fine_search_cor(sino_180, start, radius, step, ratio=0.5, denoise=True, ncore=None, hor_drop=None, ver_drop=None)[source]

Find the center-of-rotation (COR) using sub-pixel shifting.

Parameters

  • sino_180 (array_like) – 2D array. 180-degree sinogram.

  • start (float) – Starting point for searching COR.

  • radius (float) – Searching range: [start - radius; start + radius].

  • step (float) – Searching step.

  • ratio (float) – Ratio between a sample and the width of the sinogram.

  • denoise (bool, optional) – Apply a smoothing filter.

  • ncore (int or None) – Number of cpu-cores used for computing. Automatically selected if None.

  • hor_drop (int or None, optional) – Refer the method of “make_inverse_double_wedge_mask”

  • ver_drop (int or None, optional) – Refer the method of “make_inverse_double_wedge_mask”

Returns

float – Center of rotation.

algotom.prep.calculation.downsample_cor(image, dsp_fact0, dsp_fact1)[source]

Downsample an image by averaging.

Parameters

  • image (array_like) – 2D array.

  • dsp_fact0 (int) – Downsampling factor along axis 0.

  • dsp_fact1 (int) – Downsampling factor along axis 1.

Returns

array_like – 2D array. Downsampled image.

algotom.prep.calculation.find_center_vo(sino_180, start=None, stop=None, step=0.25, radius=4, ratio=0.5, dsp=True, ncore=None, hor_drop=None, ver_drop=None)[source]

Find the center-of-rotation using the method described in Ref. [1].

Parameters

  • sino_180 (array_like) – 2D array. 180-degree sinogram.

  • start (float) – Starting point for searching CoR. Use the value of (width/2 - width/16) if None.

  • stop (float) – Ending point for searching CoR. Use the value of (width/2 + width/16) if None.

  • step (float) – Sub-pixel accuracy of estimated CoR.

  • radius (float) – Searching range with the sub-pixel step.

  • ratio (float) – Ratio between the sample and the width of the sinogram.

  • dsp (bool) – Enable/disable downsampling.

  • ncore (int or None) – Number of cpu-cores used for computing. Automatically selected if None.

  • hor_drop (int or None, optional) – Refer the method of “make_inverse_double_wedge_mask”

  • ver_drop (int or None, optional) – Refer the method of “make_inverse_double_wedge_mask”

Returns

float – Center-of-rotation.

References

[1] : https://doi.org/10.1364/OE.22.019078

algotom.prep.calculation.calculate_curvature(list_metric)[source]

Calculate the curvature of a fitted curve going through the minimum value of a metric list.

Parameters

list_metric (array_like) – 1D array. List of metrics.

Returns

  • curvature (float) – Quadratic coefficient of the parabola fitting.

  • min_pos (float) – Position of the minimum value with sub-pixel accuracy.

algotom.prep.calculation.correlation_metric(mat1, mat2)[source]

Calculate the correlation metric. Smaller metric corresponds to better correlation.

Parameters

  • mat1 (array_like)

  • mat2 (array_like)

Returns

float – Correlation metric.

algotom.prep.calculation.search_overlap(mat1, mat2, win_width, side, denoise=True, norm=False, use_overlap=False, ncore=None)[source]

Calculate the correlation metrics between a rectangular region, defined by the window width, on the utmost left/right side of image 2 and the same size region in image 1 where the region is slided across image 1.

Parameters

  • mat1 (array_like) – 2D array. Projection image or sinogram image.

  • mat2 (array_like) – 2D array. Projection image or sinogram image.

  • win_width (int) – Width of the searching window.

  • side ({0, 1}) – Only two options: 0 or 1. It is used to indicate the overlap side respects to image 1. “0” corresponds to the left side. “1” corresponds to the right side.

  • denoise (bool, optional) – Apply the Gaussian filter if True.

  • norm (bool, optional) – Apply the normalization if True.

  • use_overlap (bool, optional) – Use the combination of images in the overlap area for calculating correlation coefficients if True.

  • ncore (int or None) – Number of cpu-cores used for computing. Automatically selected if None.

Returns

  • list_metric (array_like) – 1D array. List of the correlation metrics.

  • offset (int) – Initial position of the searching window where the position corresponds to the center of the window.

algotom.prep.calculation.find_overlap(mat1, mat2, win_width, side=None, denoise=True, norm=False, use_overlap=False, ncore=None)[source]

Find the overlap area and overlap side between two images (Ref. [1]) where the overlap side referring to the first image.

Parameters

  • mat1 (array_like) – 2D array. Projection image or sinogram image.

  • mat2 (array_like) – 2D array. Projection image or sinogram image.

  • win_width (int) – Width of the searching window.

  • side ({None, 0, 1}, optional) – Only there options: None, 0, or 1. “None” corresponding to fully automated determination. “0” corresponding to the left side. “1” corresponding to the right side.

  • denoise (bool, optional) – Apply the Gaussian filter if True.

  • norm (bool, optional) – Apply the normalization if True.

  • use_overlap (bool, optional) – Use the combination of images in the overlap area for calculating correlation coefficients if True.

  • ncore (int or None) – Number of cpu-cores used for computing. Automatically selected if None.

Returns

  • overlap (float) – Width of the overlap area between two images.

  • side (int) – Overlap side between two images.

  • overlap_position (float) – Position of the window in the first image giving the best correlation metric.

References

[1] : https://doi.org/10.1364/OE.418448

algotom.prep.calculation.find_overlap_multiple(list_mat, win_width, side=None, denoise=True, norm=False, use_overlap=False, ncore=None)[source]

Find the overlap-areas and overlap-sides of a list of images where the overlap side referring to the previous image.

Parameters

  • list_mat (list of array_like) – List of 2D array. Projection image or sinogram image.

  • win_width (int) – Width of the searching window.

  • side ({None, 0, 1}, optional) – Only there options: None, 0, or 1. “None” corresponding to fully automated determination. “0” corresponding to the left side. “1” corresponding to the right side.

  • denoise (bool, optional) – Apply the Gaussian filter if True.

  • norm (bool, optional) – Apply the normalization if True.

  • use_overlap (bool, optional) – Use the combination of images in the overlap area for calculating correlation coefficients if True.

  • ncore (int or None) – Number of cpu-cores used for computing. Automatically selected if None.

Returns

list_overlap (list of tuple of floats) – List of [overlap, side, overlap_position]. overlap : Width of the overlap area between two images. side : Overlap side between two images. overlap_position : Position of the window in the first image giving the best correlation metric.

algotom.prep.calculation.find_center_360(sino_360, win_width, side=None, denoise=True, norm=False, use_overlap=False, ncore=None)[source]

Find the center-of-rotation (COR) in a 360-degree scan with offset COR use the method presented in Ref. [1].

Parameters

  • sino_360 (array_like) – 2D array. 360-degree sinogram.

  • win_width (int) – Window width used for finding the overlap area.

  • side ({None, 0, 1}, optional) – Overlap size. Only there options: None, 0, or 1. “None” corresponding to fully automated determination. “0” corresponding to the left side. “1” corresponding to the right side.

  • denoise (bool, optional) – Apply the Gaussian filter if True.

  • norm (bool, optional) – Apply the normalization if True.

  • use_overlap (bool, optional) – Use the combination of images in the overlap area for calculating correlation coefficients if True.

  • ncore (int or None) – Number of cpu-cores used for computing. Automatically selected if None.

Returns

  • cor (float) – Center-of-rotation.

  • overlap (float) – Width of the overlap area between two halves of the sinogram.

  • side (int) – Overlap side between two halves of the sinogram.

  • overlap_position (float) – Position of the window in the first image giving the best correlation metric.

References

[1] : https://doi.org/10.1364/OE.418448

algotom.prep.calculation.complex_gradient(mat)[source]

Return complex gradient of a 2D array.

algotom.prep.calculation.find_shift_based_phase_correlation(mat1, mat2, gradient=True)[source]

Find relative translation in x and y direction between images with haft-pixel accuracy (Ref. [1]).

Parameters

  • mat1 (array_like) – 2D array. Projection image or sinogram image.

  • mat2 (array_like) – 2D array. Projection image or sinogram image.

  • gradient (bool, optional) – Use the complex gradient of the input image for calculation.

Returns

  • ty (float) – Translation in y-direction.

  • tx (float) – Translation in x-direction.

References

[1] : https://doi.org/10.1049/el:20030666

algotom.prep.calculation.find_center_based_phase_correlation(mat1, mat2, flip=True, gradient=True)[source]

Find the center-of-rotation (COR) using projection images at 0-degree and 180-degree.

Parameters

  • mat1 (array_like) – 2D array. Projection image at 0-degree.

  • mat2 (array_like) – 2D array. Projection image at 180-degree.

  • flip (bool, optional) – Flip the 180-degree projection in the left-right direction if True.

  • gradient (bool, optional) – Use the complex gradient of the input image for calculation.

Returns

cor (float) – Center-of-rotation.

algotom.prep.calculation.find_center_projection(mat1, mat2, flip=True, win_width=None, chunk_height=None, start_row=None, denoise=True, norm=False, use_overlap=False, ncore=None)[source]

Find the center-of-rotation (COR) using projection images at 0-degree and 180-degree based on a method in Ref. [1].

Parameters

  • mat1 (array_like) – 2D array. Projection image at 0-degree.

  • mat2 (array_like) – 2D array. Projection image at 180-degree.

  • flip (bool, optional) – Flip the 180-degree projection in the left-right direction if True.

  • win_width (int, optional) – Width of the searching window.

  • chunk_height (int or float, optional) – Height of the sub-area of projection images. If a float is given, it must be in the range of [0.0, 1.0].

  • start_row (int, optional) – Starting row used to extract the sub-area.

  • denoise (bool, optional) – Apply the Gaussian filter if True.

  • norm (bool, optional) – Apply the normalization if True.

  • use_overlap (bool, optional) – Use the combination of images in the overlap area for calculating correlation coefficients if True.

  • ncore (int or None) – Number of cpu-cores used for computing. Automatically selected if None.

Returns

cor (float) – Center-of-rotation.

References

[1] : https://doi.org/10.1364/OE.418448

algotom.prep.calculation.calculate_reconstructable_height(y_start, y_stop, pitch, scan_type)[source]

Calculate reconstructable height in a helical scan.

Parameters

  • y_start (float) – Y-position of the stage at the beginning of the scan.

  • y_stop (float) – Y-position of the stage at the end of the scan.

  • pitch (float) – The distance which the y-stage is translated in one full rotation.

  • scan_type ({“180”, “360”}) – One of two options: “180” for generating a 180-degree sinogram or “360” for generating a 360-degree sinogram.

Returns

  • y_s (float) – Starting point of the reconstructable height.

  • y_e (float) – End point of the reconstructable height.

algotom.prep.calculation.calculate_maximum_index(y_start, y_stop, pitch, pixel_size, scan_type)[source]

Calculate the maximum index of a reconstructable slice in a helical scan.

Parameters

  • y_start (float) – Y-position of the stage at the beginning of the scan.

  • y_stop (float) – Y-position of the stage at the end of the scan.

  • pitch (float) – The distance which the y-stage is translated in one full rotation.

  • pixel_size (float) – Pixel size. The unit must be the same as y-position.

  • scan_type ({“180”, “360”}) – One of two options: “180” for generating a 180-degree sinogram or “360” for generating a 360-degree sinogram.

Returns

int – Maximum index of reconstructable slices.