pysnt.filter package#

This module provides convenient access to SNT’s filter classes.

The filter module contains image processing filters for detection/enhancement of neurites and other tube-like structures

get_available_classes() → List[str][source]#

Get list of all available classes.

This includes both curated classes (always loaded) and extended classes (loaded on-demand). Extended classes are discovered if not already loaded.

Returns:

List of available class names.

Return type:

List[str]

get_class(class_name: str) → Any[source]#

Get a specific class by name.

This method provides access to both curated and extended classes. Extended classes are discovered and loaded on first access.

Supports inner class access using dot notation (e.g., “CircularModels.VonMisesFit”).

Parameters:

class_name (str) – Name of the class to retrieve. Can include inner class notation using dots (e.g., “CircularModels.VonMisesFit”).

Returns:

The requested SNT class or inner class.

Return type:

Java class

Raises:

  • KeyError – If the class is not available.

  • RuntimeError – If the JVM has not been started.

Examples

>>> # Get a regular class
>>> CircModels = pysnt.analysis.get_class("CircularModels")

>>> # Get an inner class
>>> VonMisesFit = pysnt.analysis.get_class("CircularModels.VonMisesFit")
list_classes()[source]#

Print all available classes organized by tier.

get_curated_classes() → List[str][source]#

Get list of curated classes that are always available for direct import.

Returns:

List of curated class names.

Return type:

List[str]

get_extended_classes() → List[str][source]#

Get list of extended classes available via get_class().

This will trigger discovery if not already done.

Returns:

List of extended class names.

Return type:

List[str]

class Frangi(*args, **kwargs)#

Bases: object

A.F. Frangi, W.J. Niessen, K.L. Vincken, M.A. Viergever (1998). Multiscale vessel enhancement filtering. In Medical Image Computing and Computer-Assisted Intervention - MICCAI'98, W.M. Wells, A. Colchester and S.L. Delp (Eds.), Lecture Notes in Computer Science, vol. 1496 - Springer Verlag, Berlin, Germany, pp. 130-137.

All Methods and Attributes: See Frangi detailed documentation.

class Tubeness(*args, **kwargs)#

Bases: object

  1. Sato, S. Nakajima, N. Shiraga, H. Atsumi, S. Yoshida, T. Koller, G. Gerig, and R. Kikinis, “Three-dimensional multi-scale line filter for segmentation and visualization of curvilinear structures in medical images,” Med Image Anal., vol. 2, no. 2, pp. 143-168, June 1998.

All Methods and Attributes: See Tubeness detailed documentation.