Free and open source

The BigStitcher is a software package that allows simple and efficient alignment of multi-tile and multi-angle image datasets, for example acquired by lightsheet, widefield or confocal microscopes. The software supports images of almost arbitrary size ranging from very small images up to volumes in the range of many terabytes, which are for example produced when acquiring cleared tissue samples with lightsheet microscopy.

FracLac is for digital image analysis. Use it to measure difficult to describe morphological features.
FracLac is a plugin for ImageJ. It is freely available software developed and maintained by our lab at the School of Community Health, Faculty of Science, Charles Sturt University, Australia. The author of the software and project lead is also the author of this document (me, Audrey Karperien). The basic box counting algorithm was originally modified from ImageJ's box counting algorithm and H. Jelinek's NIH Image plugin, and was further elaborated based on extensive research and development. The convex hull algorithm was provided by Thomas Roy, University of Alberta, Canada. As open source software, with the continuing help of a host of users and collaborators, FracLac has evolved to a suite of fractal analysis and morphology functions.

This project was designed for vectorize and analyze the  blood vessels in the mouse brain.

This plugin requires the definition of seed point detection settings by the user (Semi-automated).

We have developed a novel approach, named DF-Tracing, to tackle this challenge. This method first extracts the neurite signal (foreground) from a noisy image by using anisotropic filtering and automated thresholding. Then, DF-Tracing executes a coupled distance-field (DF) algorithm on the extracted foreground neurite signal and reconstructs the neuron morphology automatically. Two distance-transform based “force” fields are used: one for “pressure”, which is the distance transform field of foreground pixels (voxels) to the background, and another for “thrust”, which is the distance transform field of the foreground pixels to an automatically determined seed point. The coupling of these two force fields can“push” a “rolling ball” quickly along the skeleton of a neuron, reconstructing the 3D cell morphology.