__all__ = ["hessian_objectness"]
import ants
from ants.decorators import image_method
from ants.internal import get_lib_fn
[docs]
@image_method
def hessian_objectness(image,
object_dimension=1,
is_bright_object=True,
sigma_min=0.1,
sigma_max=10,
number_of_sigma_steps=10,
use_sigma_logarithmic_spacing=True,
alpha=0.5,
beta=0.5,
gamma=5.0,
set_scale_objectness_measure=True):
"""
Interface to ITK filter. Based on the paper by Westin et al., "Geometrical
Diffusion Measures for MRI from Tensor Basis Analysis" and Luca Antiga's
Insight Journal paper http://hdl.handle.net/1926/576.
Arguments
---------
image : ANTsImage
scalar image.
object_dimension : unsigned int
0: 'sphere', 1: 'line', or 2: 'plane'.
is_bright_object : boolean
Set 'true' for enhancing bright objects and 'false' for dark objects.
sigma_min : float
Define scale domain for feature extraction.
sigma_max : float
Define scale domain for feature extraction.
number_of_sigma_steps : unsigned int
Define number of samples for scale space.
use_sigma_logarithmic_spacing : boolean
Define sample spacing the for scale space.
alpha: float
Hessian filter parameter.
beta: float
Hessian filter parameter.
gamma: float
Hessian filter parameter.
set_scale_objectness_measure: boolean
...
Returns
-------
ANTsImage
Example
-------
>>> import ants
>>> image = ants.image_read(ants.get_ants_data('r16'))
>>> hessian_object_image = ants.hessian_objectness(image)
"""
image_dimension = image.dimension
libfn = get_lib_fn('hessianObjectnessF%i' % image_dimension)
hessian = libfn(image.pointer, object_dimension, is_bright_object, sigma_min,
sigma_max, number_of_sigma_steps,
use_sigma_logarithmic_spacing, alpha,
beta, gamma, set_scale_objectness_measure)
output_image = ants.from_pointer(hessian).clone('float')
return output_image