itk_vesselness

apply vesselness filter for vessel-like structure enhancement

itk_vesselness <input> <output> [options]

DESCRIPTION

itk_vesselness applies a Hessian-based vesselness filter (Frangi filter) to a volume using the Insight Toolkit (ITK). The filter enhances tubular structures such as blood vessels by analysing the eigenvalues of the Hessian matrix at each voxel. It produces a response map where high values indicate a strong likelihood of vessel-like geometry.

A multi-scale approach is supported through --min-sigma, --max-sigma, and --steps, which allow the filter to detect vessels across a range of diameters. At each scale, a Gaussian smoothing with the corresponding sigma is applied before Hessian computation, and the maximum response across all scales is retained.

The sensitivity of the filter is controlled by --alpha, --beta, and --gamma, which weight the contributions of the plate-like, blob-like, and structureness measures respectively.

OPTIONS

--verbose

Print verbose information during processing.

--clobber

Overwrite the output file if it already exists.

--sigma val

Set a single Gaussian sigma value (in mm) for single-scale vesselness computation.

--min-sigma val

Minimum sigma for multi-scale vesselness computation (in mm).

--max-sigma val

Maximum sigma for multi-scale vesselness computation (in mm).

--steps n

Number of discrete sigma steps between min-sigma and max-sigma for multi-scale analysis.

--alpha val

Sensitivity parameter for plate-like structure suppression. Controls the weight of the ratio between the two largest eigenvalues.

--beta val

Sensitivity parameter for blob-like structure suppression. Controls the weight of the ratio that distinguishes blobs from tubes.

--gamma val

Sensitivity parameter for the structureness (Frobenius norm) term. Controls the overall background noise suppression.

--float

Store output voxels as 32-bit floating point.

--double

Store output voxels as 64-bit double-precision floating point.

EXAMPLES

Apply single-scale vesselness enhancement:

itk_vesselness input.mnc vessels.mnc --sigma 1.0 --float

Apply multi-scale vesselness across a range of vessel diameters:

itk_vesselness input.mnc vessels.mnc --min-sigma 0.5 --max-sigma 3.0 --steps 5 --float

Adjust sensitivity parameters for specific vessel characteristics:

itk_vesselness input.mnc vessels.mnc --sigma 1.5 --alpha 0.5 --beta 0.5 --gamma 5.0 --float --clobber

AUTHOR

Vladimir S. Fonov - Montreal Neurological Institute, McGill University.

COPYRIGHTS

Copyright (C) Vladimir S. Fonov, McConnell Brain Imaging Centre, McGill University.

SEE ALSO

itk_laplace, itk_diffusion, mincblur, minccalc