Image feature detection

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.

WND-CHARM is a multi-purpose image classifier that can be applied to a wide variety of image classification tasks without modifications or fine-tuning, and yet provides classification accuracy comparable to state-of-the-art task-specific image classifiers. WND-CHARM can extract up to ~3,000 generic image descriptors (features) including polynomial decompositions, high contrast features, pixel statistics, and textures. These features are derived from the raw image, transforms of the image, and compound transforms of the image (transforms of transforms). The features are filtered and weighted depending on their effectiveness in discriminating between a set of predefined image classes (the training set). These features are then used to classify test images based on their similarity to the training classes. This classifier was tested on a wide variety of imaging problems including biological and medical image classification using several imaging modalities, face recognition, and other pattern recognition tasks. WND-CHARM is an acronym that stands for "Weighted Neighbor Distance using Compound Hierarchy of Algorithms Representing Morphology."

This is a Matlab implementation of Local Phase Quantization (LPQ) texture descriptors that is robust to image blurring due to the use of phase information. Theoretical background could be found here: http://www.ee.oulu.fi/research/mvmp/mvg/files/pdf/ICISP08.pdf