estimate_bspline_transform#
- estimate_bspline_transform(fixed_image, moving_image, fixed_mask, moving_mask, **kwargs)[source]#
Estimate B-spline transformation.
This function performs registration using the SimpleITK toolkit. We employed
a deformable registration using a multi-resolution B-spline approach. B-spline registration uses B-spline curves to compute the deformation field mapping pixels in a moving image to corresponding pixels in a fixed image.
- Parameters:
fixed_image (
numpy.ndarray
) – A fixed image.moving_image (
numpy.ndarray
) – A moving image.fixed_mask (
numpy.ndarray
) – A binary tissue mask for the fixed image.moving_mask (
numpy.ndarray
) – A binary tissue mask for the moving image.**kwargs (dict) –
- Key-word arguments for B-spline parameters.
- grid_space (float):
Grid_space (mm) to decide control points.
- scale_factors (list):
Scaling factor of each B-spline per level in a multi-level setting.
- shrink_factor (list):
Shrink factor per level to change the size and complexity of the image.
- smooth_sigmas (list):
Standard deviation for gaussian smoothing per level.
- num_iterations (int):
Maximal number of iterations.
- sampling_percent (float):
Fraction of image used for metric evaluation.
- learning_rate (float):
Step size along traversal direction in parameter space.
- convergence_min_value (float):
Value for checking convergence together with energy profile of the similarity metric.
- convergence_window_size (int):
Number of similarity metric values for estimating the energy profile.
- Returns:
2D deformation transformation represented by a grid of control points.
- Return type:
BSplineTransform