ants.ops.hessian_objectness

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)[source]

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.

Parameters:

  • image (ants.core.ANTsImage) – scalar image.

  • object_dimension (unsigned int) – 0: ‘sphere’, 1: ‘line’, or 2: ‘plane’.

  • is_bright_object (bool) – 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 (bool) – 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 (bool) –

    …

Return type:

ants.core.ANTsImage

Example

>>> import ants
>>> image = ants.image_read(ants.get_ants_data('r16'))
>>> hessian_object_image = ants.hessian_objectness(image)