Tried hard to complete the code for diffeomorphic registration of images but evry time I got some challenge to overcome. Please have a look If any one can complete it and run it.
most of it was done except converging criteria. But unnecessarily huge code and bugs made me crazy. Thanks for the help.
symmetric diffeomorphic image registration is better than affine transformation only. It is a non-linear process.
import numpy as np
# from scipy.ndimage import map_coordinates
# class DiffeomorphicMap:
# def __init__(self, domain_grid, affine):
# self.forward = None
# self.backward = None
# def set_forward_transform(self, transform):
# self.forward = transform
# def set_backward_transform(self, transform):
# self.backward = transform
# def transform(self, image, mode='linear', cval=0.0, order=1):
# """
# Apply the forward transform to the image.
# """
# coords = np.indices(image.shape).reshape(3, -1)
# transformed_coords = self.forward(coords).reshape(image.shape + (3,))
# transformed_image = map_coordinates(image, transformed_coords, order=order, mode=mode, cval=cval)
# return transformed_image
# def transform_inverse(self, image, mode='linear', cval=0.0, order=1):
# """
# Apply the inverse transform to the image.
# """
# if self.backward is None:
# raise ValueError("Backward transformation is not set.")
# coords = np.indices(image.shape).reshape(3, -1)
# transformed_coords = self.backward(coords).reshape(image.shape + (3,))
# transformed_image = map_coordinates(image, transformed_coords, order=order, mode=mode, cval=cval)
# return transformed_image
# def compute_inversion_error(self):
# """
# Compute the inversion error by comparing forward and backward transformations.
# """
# if self.forward is None or self.backward is None:
# raise ValueError("Forward and backward transformations must be set before computing inversion error.")
# grid_shape = self.forward.shape[:-1]
# identity_grid = np.indices(grid_shape).transpose(1, 2, 3, 0)
# forward_coords = self.forward.reshape(-1, 3)
# backward_coords = self.backward(forward_coords).reshape(grid_shape + (3,))
# error = np.linalg.norm(identity_grid - backward_coords, axis=-1)
# residual = np.mean(error)
# stats = (np.min(error), np.mean(error), np.max(error))
# return residual, stats
import numpy as np
from scipy.ndimage import map_coordinates
import numpy as np
from scipy.ndimage import gaussian_filter, zoom
class DiffeomorphicMap:
def __init__(self):
self.forward = None
self.backward = None
def set_forward_transform(self, transform):
self.forward = transform
def set_backward_transform(self, transform):
self.backward = transform
def transform(self, image, mode='linear', cval=0.0, order=1):
"""
Apply the forward transform to the image.
"""
coords = np.indices(image.shape).reshape(3, -1)
transformed_coords = self.forward(coords).reshape(image.shape + (3,))
transformed_image = map_coordinates(image, transformed_coords, order=order, mode=mode, cval=cval)
return transformed_image
def transform_inverse(self, image, order=1, mode='constant', cval=0.0):
"""
Apply the inverse transform to the image.
"""
if self.backward is None:
raise ValueError("Backward transformation is not set.")
coords = np.indices(image.shape).reshape(3, -1)
transformed_coords = self.backward(coords).reshape(image.shape + (3,))
transformed_image = map_coordinates(image, transformed_coords, order=order, mode=mode, cval=cval)
return transformed_image
def compute_inversion_error(self):
"""
Compute the inversion error by comparing forward and backward transformations.
"""
if self.forward is None or self.backward is None:
raise ValueError("Forward and backward transformations must be set before computing inversion error.")
grid_shape = self.forward.shape[:-1]
identity_grid = np.indices(grid_shape).transpose(1, 2, 3, 0)
forward_coords = self.forward.reshape(-1, 3)
backward_coords = self.backward(forward_coords).reshape(grid_shape + (3,))
error = np.linalg.norm(identity_grid - backward_coords, axis=-1)
residual = np.mean(error)
stats = (np.min(error), np.mean(error), np.max(error))
return residual, stats
class MultiscaleImage:
def __init__(self, image, grid2world, spacing):
self.image = image
self.grid2world = grid2world
self.spacing = spacing
self.levels = 3 # Example: Set the number of levels for demonstration
self.pyramid = self.gaussian_pyramid(levels=self.levels, sigmas=[1.0] * self.levels, down_facts=[2] * self.levels)
def get_image(self, level):
"""
Get the image at the specified level.
Parameters:
- level (int): The level of the image to retrieve.
Returns:
- ndarray: The image at the specified level.
"""
# Ensure level is within valid range
if level < 0 or level >= self.levels:
raise ValueError("Invalid level specified.")
# Return the image at the specified level
return self.pyramid[level]
def get_domain_shape(self, level):
"""
Get the shape of the image domain at the specified level.
Parameters:
- level (int): The level for which to get the domain shape.
Returns:
- tuple: The shape of the image domain at the specified level.
"""
# Ensure level is within valid range
if level < 0 or level >= self.levels:
raise ValueError("Invalid level specified.")
# Return the shape of the image at the specified level
return self.get_image(level).shape
def get_affine_inv(self, level):
"""
Get the inverse affine transformation matrix at the specified level.
Parameters:
- level (int): The level for which to get the inverse affine transformation matrix.
Returns:
- ndarray: The inverse affine transformation matrix at the specified level.
"""
# Ensure level is within valid range
if level < 0 or level >= self.levels:
raise ValueError("Invalid level specified.")
# Example implementation: Return the inverse of the grid2world transformation matrix
return np.linalg.inv(self.grid2world)
def get_spacing(self, level):
"""
Get the spacing (voxel size) at the specified level.
Parameters:
- level (int): The level for which to get the spacing.
Returns:
- tuple: The spacing (voxel size) at the specified level.
"""
# Ensure level is within valid range
if level < 0 or level >= self.levels:
raise ValueError("Invalid level specified.")
# Example implementation: Return the spacing at the specified level
return self.spacing
def gaussian_pyramid(self, levels, sigmas, down_facts):
new_imgs = [self.image]
for i in range(levels):
image = new_imgs[-1]
smooth_img = gaussian_filter(image, sigma=sigmas[i])
small_img = zoom(smooth_img, 1 / down_facts[i], order=1)
new_imgs.append(small_img)
return new_imgs
class SymmetricDiffeomorphicRegistration:
def __init__(self, metric, level_iters=None, step_length=0.25,
ss_sigma_factor=0.2, opt_tol=1e-5, inv_iter=20,
inv_tol=1e-3, callback=None, verbosity=VerbosityLevels.DEFAULT):
self.metric = metric
self.level_iters = level_iters if level_iters is not None else [100, 100, 25]
self.step_length = step_length
self.ss_sigma_factor = ss_sigma_factor
self.opt_tol = opt_tol
self.inv_iter = inv_iter
self.inv_tol = inv_tol
self.callback = callback
self.verbosity = verbosity
self.niter = 0
self.max_iterations = sum(self.level_iters)
def _init_optimizer(self, static, moving, static_grid2world, moving_grid2world, prealign):
# Initialize necessary components for optimization
self.static = static
self.moving = moving
self.static_grid2world = static_grid2world
self.moving_grid2world = moving_grid2world
self.prealign = prealign
self.static_ss = MultiscaleImage(static, static_grid2world, (1, 1, 1)) # Example spacing
self.moving_ss = MultiscaleImage(moving, moving_grid2world, (1, 1, 1)) # Example spacing
self.static_to_ref = DiffeomorphicMap()
self.moving_to_ref = DiffeomorphicMap() # Initialize moving to reference map as well
def _converged(self):
return self.niter >= self.max_iterations
def _iterate(self):
current_moving = self.moving_ss.get_image(self.current_level)
current_static = self.static_ss.get_image(self.current_level)
current_disp_shape = self.static_ss.get_domain_shape(self.current_level)
current_disp_grid2world = self.static_ss.get_affine(self.current_level)
current_disp_world2grid = self.static_ss.get_affine_inv(self.current_level)
current_disp_spacing = self.static_ss.get_spacing(self.current_level)
# Set a simple backward transformation for demonstration
self.static_to_ref.set_backward_transform(lambda coords: coords)
self.moving_to_ref.set_backward_transform(lambda coords: coords)
wstatic = self.static_to_ref.transform_inverse(current_static, order=1, mode='constant', cval=0.0)
wmoving = self.moving_to_ref.transform_inverse(current_moving, order=1, mode='constant', cval=0.0)
# Optimization logic here...
def _finalize_optimizer(self):
pass
def optimize(self, static_image, moving_image, static_grid2world, moving_grid2world, prealign):
self._init_optimizer(static_image, moving_image, static_grid2world, moving_grid2world, prealign)
while not self._converged():
self._iterate()
self._finalize_optimizer()
return self.static_to_ref
# # Usage example:
# # Create a SimpleSimilarityMetric instance
metric = SimpleSimilarityMetric(dim=3)
# # Set static and moving images
static_img = np.random.rand(10, 10, 10)
moving_img = np.random.rand(10, 10, 10)
metric.set_static_image(static_img)
metric.set_moving_image(moving_img)
# # Set the mask0 if necessary
metric.set_mask0(static_img) # Replace mask0 with the actual mask0 data
# # Create an instance of SymmetricDiffeomorphicRegistration
registration = SymmetricDiffeomorphicRegistration(metric=metric, verbosity=VerbosityLevels.STATUS)
# # Call the optimize function to perform registration
result_map = registration.optimize(static_image, moving_image, static_grid2world,
moving_grid2world, prealign)
# # result_map to transform images or points