Component

This plugin filters a 3D image stack (or 2D image) to produce a score for how "tube-like" each point in the image is. This is useful as a preprocessing step for tracing neurons or blood vessels, for example. For 3D image stacks, the plugin uses the eigenvalues of the Hessian matrix to calculate this measure of "tubeness", using a metrics mentioned in Sato et al 1997 ¹: if the larger two eigenvalues (λ₂ and λ₃) are both negative then value is √(λ₂λ₃), otherwise the value is 0. For 2D images, if the large eigenvalue is negative, we return its absolute value and otherwise return 0.

This plugin is now bundled as part of Fiji.

JFilament is an ImageJ plugin for segmentation and tracking of 2D and 3D filaments in fluorescenece microscopy images. The main algorithm used in Jfilament is "Stretching Open Active Contours" (SOAC). In order to use this method, the user must define seed points in the image where the SOAC method will begin.

JFilament also includes 2D "closed" active contours which can be used for tasks such as segmentation and tracking of cell boundaries.

This plugin is used to infer the preferred orientation of structures present in the input image. It computes a histogram indicating the amount of structures in a given direction. Images with completely isotropic content are expected to give a flat histogram, whereas images in which there is a preferred orientation are expected to give a histogram with a peak at that orientation. On top of the histogram, the plugin tries to generate statistics on the highest peak found.

The plugin offers the possibility to generate an orientation map, where the image is colored according to its local directionality, or location orientation. 

The plugin is part of Fiji, can be launched through the menu: Analyze > Directionality

Measures wound-healing assay videos, 

 For each video, the velocity and the order parameter are analyzed in time and space to extract quantitative parameters characterizing the cell motility phenotype. The different conditions (videos) can then be classified according to these parameters.

Computes image Hessian
Based on the algorithm described in the paper below. 

Splines: A Perfect Fit for Signal and Image Processing
M. Unser
IEEE Signal Processing Magazine, vol. 16, no. 6, pp. 22-38, November 1999.
 DOI: 10.1109/79.799930
 http://ieeexplore.ieee.org/document/799930/