esmraldi.registration¶
Module for the registration of two images
Module Contents¶
Functions¶
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Precision between two images |
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Recall between two images |
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Evaluates registration quality. |
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Computes the F-Measure, or F1-score, |
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Mutual information for joint histogram |
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Computes the mutual information on DT |
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Finds the best fit between variations of the same image |
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Utility function to setup the resampler |
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Function returning the similarity metric value |
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Function returning the similarity metric value |
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Function used for the registration of NMF component images. |
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Registration between reference (fixed) |
- esmraldi.registration.precision(im1, im2)¶
Precision between two images defined as card(im1 cap im2)/card(im2)
- Parameters
- im1: np.ndarray
first binary image
- im2: np.ndarray
second binary image
- Returns
- float
precision value
- esmraldi.registration.recall(im1, im2)¶
Recall between two images defined as card(im1 cap im2)/card(im1)
- Parameters
- im1: np.ndarray
first binary image
- im2: np.ndarray
second binary image
- Returns
- float
recall value
- esmraldi.registration.quality_registration(imRef, imRegistered, threshold=- 1, display=False)¶
Evaluates registration quality.
Binarizes images, then computes recall and precision.
- Parameters
- imRef: np.ndarray
reference (fixed) image
- imRegistered: np.ndarray
deformable (moving) image - after registration
- threshold: int
threshold to get binary images. A value of -1 means using Otsu thresholding scheme.
- Returns
- tuple
precision and recall values
- esmraldi.registration.fmeasure(precision, recall)¶
Computes the F-Measure, or F1-score, that is the harmonic mean of the precision and recall.
- Parameters
- precision: float
precision value
- recall: float
recall value
- Returns
- float
fmeasure
- esmraldi.registration.mutual_information(imRef, imRegistered, bins=20)¶
Mutual information for joint histogram based on entropy computation
- Parameters
- imRef: np.ndarray
reference (fixed) image
- imRegistered: np.ndarray
deformable (moving)image
- bins: int
number of bins for joint histogram
- Returns
- float
mutual information
- esmraldi.registration.dt_mutual_information(imRef, imRegistered, bins=20)¶
Computes the mutual information on DT
- Parameters
- imRef: sitk.Image
reference image
- imRegistered: sitk.Image
target image
- bins: int
number of bins for mutual information
- Returns
- float
mutual information between DT of images
- esmraldi.registration.best_fit(fixed, array_moving, number_of_bins, sampling_percentage, find_best_rotation=False, learning_rate=1.1, min_step=0.001, relaxation_factor=0.8)¶
Finds the best fit between variations of the same image according to mutual information measure.
Different variations of the image (e.g. symmetry) are stored in the first dimension of the array.
- Parameters
- fixed: np.ndarray
reference (fixed) image
- array_moving: np.ndarray
3D deformable (moving) image
- number_of_bins: int
number of bins for sampling
- sampling_percentage: float
proportion of points to consider in sampling
- find_best_rotation: bool
whether to find the best rotation
- learning_rate: float
learning rate for gradient descent optimization
- min_step: float
min step for optimizer
- relaxation_factor: float
relaxation factor for optimizer
- Returns
- sitk.ImageRegistrationMethod
registration object
- esmraldi.registration.initialize_resampler(fixed, tx)¶
Utility function to setup the resampler i.e. the object which can apply a transform onto an image.
Initialized with nearest neighbor interpolation.
- Parameters
- fixed: sitk.Image
reference image
- tx: sitk.Transform
the transform
- Returns
- sitk.ResampleImageFilter
the resampler
- esmraldi.registration.find_best_transformation(scale_and_rotation, initial_transform, fixed, moving, update_DT=True)¶
Function returning the similarity metric value between the reference image and the deformed target image.
The current transformation parameters (scale and rotation) are applied to the target image before estimating the metric, i.e. either regular mean squared error, or mean squared error on the distance transformed images.
- Parameters
- scale_and_rotation: list
current scale and rotation parameters
- initial_transform: sitk.Transform
initial transform
- fixed: sitk.Image
reference image
- moving: sitk.Image
target image
- update_DT: bool
whether to re-compute the DT at each step (DT-MSE)
- Returns
- float
similarity metric value
- esmraldi.registration.find_best_translation(translation_vector, initial_transform, fixed, moving)¶
Function returning the similarity metric value between the reference image and the deformed target image. This function is used in the context of NMF component image registration.
The current transformation parameters (only translation) are applied to the target image before estimating the metric, i.e. mean squared error.
- Parameters
- translation_vector: list
translation vector in nD
- initial_transform: sitk.Transform
the initial transform
- fixed: sitk.Image
reference image
- moving: sitk.Image
moving image
- Returns
- float
similarity metric value
- esmraldi.registration.register_component_images(fixed_array, moving_array, component_images_array, translation_range=1)¶
Function used for the registration of NMF component images.
Translation of component images to match the reference.
- Parameters
- fixed_array: np.ndarray
reference image
- moving_array: np.ndarray
target image
- component_images_array: np.ndarray
NMF component images: shape (w, h, number_of_components)
- translation_range: int
translation search between [-t, t], default 1
- Returns
- tuple(np.ndarray, np.ndarray)
The translated NMF component images, the translation map
- esmraldi.registration.register(fixed, moving, number_of_bins, sampling_percentage, find_best_rotation=False, use_DT=True, update_DT=False, normalize_DT=False, seed=1, learning_rate=1.1, min_step=0.001, relaxation_factor=0.8)¶
Registration between reference (fixed) and deformable (moving) images.
The transform is initialized with moments
Metric: mutual information
Optimization: gradient descent
Interpolation: nearest neighbor
- Parameters
- fixed: np.ndarray
reference (fixed) image
- moving: np.ndarray
deformable (moving) image
- number_of_bins: int
number of bins for sampling
- sampling_percentage: float
proportion of points to consider in sampling
- find_best_rotation: bool
whether to use exhaustive search to find the best scaling and rotation parameters
- use_DT: bool
whether to use the distance transformations of images during exhaustive optimization (if find_best_rotation==True)
- update_DT: bool
whether to update the distance transformations during exhaustive optimization (if find_best_rotation==True)
- normalize_DT: bool
whether to normalize the distance transformations during exhaustive optimization ((if find_best_rotation==True)
- seed: int
seed for metric sampling
- learning_rate: float
learning rate for gradient descent optimizer
- min_step: float
minimum step: stop criterion for the optimizer
- relaxation_factor: float
relaxation factor for the parameters of the transform between each step of the optimizer
- Returns
- sitk.ImageRegistrationMethod
registration object