OrientationJ¶

Fibers, filaments, fringes, crack,fractures, flows, growth rings: many images are made of elongated structures, and what matters about them is not their intensity but the direction they follow. OrientationJ measures that direction everywhere in the image, together with how consistently it holds and how strongly it stands out from the background.

At every pixel the plugins evaluate the gradient structure tensor over a small window and extract the orientation of the local structure, the coherency telling whether that orientation is well defined or the neighborhood is isotropic, and the energy telling whether there is any structure at all. From these come color surveys, vector fields, angular histograms and per-region measurements — figures to look at and numbers to report.

Drag the handle: collagen fibers on the left, the same field as a color survey on the right — hue gives the orientation, saturation the coherency.

The suite covers the whole workflow. Analysis produces the feature maps and the color survey; Distribution turns them into an angular histogram; Vector Field overlays a readable field of directions; Measure and Dominant Direction report numbers for a selection or a whole image; Clustering and Horizontal Alignment group and straighten oriented regions; Corner Harris detects keypoints from the same tensor; Test Image generates chirps and stacks to calibrate on; and MonogenicJ extends the analysis to a multiresolution monogenic representation. Every command runs from a dialog and from an ImageJ macro, and all of them share the same two core settings — the analysis scale σ and the gradient — described in How to use. The animated banner above sweeps σ on the classic Tree Rings sample: small windows follow every detail, large ones summarize the trend (macro).

The mathematics behind the measurement, from the weighted inner product to the tensor invariants, is derived in Theory. The orientation distribution of OrientationJ is compared with six other tools on a common dataset in Benchmarking, and the images used throughout are published in Test images. Two Python versions accompany the plugin: the faithful Python port, which reproduces the plugin bit for bit, and the minimal GST operator, a forward model with its blind inverse.

If OrientationJ contributed to your results, please cite the publication matching what you used — the method, the angular distribution, the local measurements, or the monogenic analysis:

Püspöki Z, Storath M, Sage D, Unser M (2016). Transforms and Operators for Directional Bioimage Analysis: A Survey. Advances in Anatomy, Embryology and Cell Biology, vol. 219, Focus on Bio-Image Informatics, Springer. doi:10.1007/978-3-319-28549-8_3

Rezakhaniha R, Agianniotis A, Schrauwen JTC, Griffa A, Sage D, Bouten CVC, van de Vosse FN, Unser M, Stergiopulos N (2012). Experimental Investigation of Collagen Waviness and Orientation in the Arterial Adventitia Using Confocal Laser Scanning Microscopy. Biomechanics and Modeling in Mechanobiology 11(3–4): 461–473. doi:10.1007/s10237-011-0325-z

Fonck E, Feigl GG, Fasel J, Sage D, Unser M, Rüfenacht DA, Stergiopulos N (2009). Effect of Aging on Elastin Functionality in Human Cerebral Arteries. Stroke 40(7): 2552–2556. doi:10.1161/STROKEAHA.108.528091

Unser M, Sage D, Van De Ville D (2009). Multiresolution Monogenic Signal Analysis Using the Riesz–Laplace Wavelet Transform. IEEE Transactions on Image Processing 18(11): 2402–2418. doi:10.1109/TIP.2009.2027628

An annotated table of the publications that use and cite OrientationJ is on the In the literature page, and the release notes are in the version history.