esmraldi.imageutils¶
Module Contents¶
Functions¶
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Center 2D images w.r.t. to the center of the image size, |
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Resize the image to a given size. |
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Get largest area |
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Get area relative to largest area |
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Enforce monotonic distribution of sequence |
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Find slice correspondences with Dynamic Time Warping |
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Find slice correspondences manually |
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Compute DT |
Estimates the noise in an image |
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Variance image |
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Standard deviation image |
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Mean squared error on numpy arrays |
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Mean squared error on ITK Images |
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Mean squared error on distance transformed |
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Mean squared error on stddev |
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Computes an image which maps each point to the radius of its enclosing |
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Normalized distance transformation |
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Identify local maxima in the distance |
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Export array as figure in original resolution |
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Computes simple Voronoi diagram |
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Basic and Simplified Voronoi Covariance Measure |
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Normal plane estimation with VCM |
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Local radius from VCM plane estimation |
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- esmraldi.imageutils.center_images(images, size)¶
Center 2D images w.r.t. to the center of the image size, and superimpose them to create a 3D volume
- Parameters
- images: list
list of 2D images
- size: tuple
desired image size
- Returns
- np.ndarray
superimposed 2D images
- esmraldi.imageutils.resize(image, size)¶
Resize the image to a given size.
- Parameters
- image: sitk.Image
input image
- size: tuple
new size of the image
- Returns
- sitk.Image
new resized image
- esmraldi.imageutils.max_area_slices(image)¶
Get largest area
- esmraldi.imageutils.relative_area(image)¶
Get area relative to largest area
- esmraldi.imageutils.enforce_continuity_values(sequence)¶
Enforce monotonic distribution of sequence
- esmraldi.imageutils.slice_correspondences(reference, target, sigma, is_reversed=False, is_continuity=True)¶
Find slice correspondences with Dynamic Time Warping
- Parameters
- reference: np.ndarray
reference image
- target: np.ndarray
target
- sigma: float
gaussian standard deviation
- is_reversed: bool
whether the images are reversed in the z-axis
- is_continuity: bool
whether to enforce continuity (slice numbers are monotonically increasing)
- Returns
- np.ndarray
correspondence indices
- esmraldi.imageutils.slice_correspondences_manual(reference, target, resolution_reference, resolution_target, slices_reference, slices_target, is_reversed=False)¶
Find slice correspondences manually
- Parameters
- reference: np.ndarray
reference image
- target: np.ndarray
target
- resolution_reference: float
interslice resolution reference
- resolution_target: float
interslice resolution target
- slices_reference: list
slice numbers reference
- slices_target: list
slice numbers target
- is_reversed: bool
whether the images are reversed in the z-axis
- Returns
- np.ndarray
correspondence indices
- esmraldi.imageutils.compute_DT(image_itk, invert=False)¶
Compute DT
- Parameters
- image_itk: sitk.Image
ITK Image
- Returns
- sitk.Image
DT Image
- esmraldi.imageutils.estimate_noise(I)¶
Estimates the noise in an image by convolution with a kernel.
See: Fast Noise Variance Estimation, Immerkaear et al.
- Parameters
- I: np.ndarray
image
- Returns
- float
the noise standard deviation
- esmraldi.imageutils.variance_image(image, size=3)¶
Variance image
- Parameters
- image: np.ndarray
the image
- size: int
neighborhood size
- Returns
- np.ndarray
variance image
- esmraldi.imageutils.stddev_image(image, size=3)¶
Standard deviation image
- Parameters
- image: np.ndarray
the image
- size: int
neighborhood size
- Returns
- np.ndarray
stddev image
- esmraldi.imageutils.mse_numpy(fixed_array, moving_array)¶
Mean squared error on numpy arrays
- Parameters
- fixed_array: np.ndarray
image 1
- moving_array: np.ndarray
image 2
- Returns
- float
Mean squared error between images
- esmraldi.imageutils.mse(fixed, moving)¶
Mean squared error on ITK Images
- Parameters
- fixed: sitk.Image
image 1
- moving: sitk.Image
image 2
- Returns
- float
Mean squared error between images
- esmraldi.imageutils.dt_mse(fixed, moving)¶
Mean squared error on distance transformed ITK images
- Parameters
- fixed: sitk.Image
image 1
- moving: sitk.Image
image 2
- Returns
- float
Mean squared error between DT images
- esmraldi.imageutils.mse_stddev(fixed, moving)¶
Mean squared error on stddev ITK images
- Parameters
- fixed: sitk.Image
image 1
- moving: sitk.Image
image 2
- Returns
- float
Mean squared error between stddev images
- esmraldi.imageutils.radius_maximal_balls(image)¶
Computes an image which maps each point to the radius of its enclosing maximal ball
- Parameters
- image: np.ndarray
the image
- Returns
- np.ndarray
maximal ball radii map
- esmraldi.imageutils.normalized_dt(image)¶
Normalized distance transformation by the maximal ball radii
- Parameters
- image: np.ndarray
input image
- Returns
- np.ndarray
normalized dt
- esmraldi.imageutils.local_max_dt(image)¶
Identify local maxima in the distance transformed ITK image
- Parameters
- image: sitk.Image
input image
- Returns
- np.ndarray
local maxima map, where local maxima = 1
- esmraldi.imageutils.export_figure_matplotlib(f_name, arr, arr2=None, dpi=200, resize_fact=1, cmaps=['gray', 'Reds'], alpha=0.5, plt_show=False, vmin=None, vmax=None)¶
Export array as figure in original resolution :param arr: array of image to save in original resolution :param f_name: name of file where to save figure :param resize_fact: resize facter wrt shape of arr, in (0, np.infty) :param dpi: dpi of your screen :param plt_show: show plot or not
- esmraldi.imageutils.voronoi_diagram(points, shape)¶
Computes simple Voronoi diagram
- Parameters
- points: np.ndarray
sites
- shape: tuple
shape of image
- Returns
- np.ndarray
Voronoi diagram as union of Voronoi cells
- esmraldi.imageutils.simple_vcm(voronoi, point, r)¶
Basic and Simplified Voronoi Covariance Measure
- Parameters
- voronoi: tuple
sites and associated Voronoi cells
- point: tuple
point where to estimate VCM
- r: radius
local radius for VCM
- Returns
- tuple
Eigenvectors associated to the Voronoi cell shape + max distance in cells
- esmraldi.imageutils.estimate_plane(obj, voronoi, point, max_r=np.inf)¶
Normal plane estimation with VCM
- Parameters
- obj: np.ndarray
object
- voronoi: tuple
sites, and associated Voronoi cells
- point: tuple
point where to estimate plane
- max_r: int
maximum bounding radius for Voronoi cells (big R)
- esmraldi.imageutils.local_radius(image)¶
Local radius from VCM plane estimation
- Parameters
- image: np.ndarray
image
- Returns
- np.ndarray
Local radius map
- esmraldi.imageutils.pseudo_flat_field_correction(image, sigma)¶
- esmraldi.imageutils.get_norm_image(images, norm, mzs)¶
- esmraldi.imageutils.normalize_image(current_image, norm_img)¶
- esmraldi.imageutils.distance_point_to_set(point, point_set)¶
- esmraldi.imageutils.rectangle_coordinates(lower_left, upper_right)¶