BaSDI
Bayesian Super-resolution Drift Inference
Single-molecule localization based super-resolution microscopy requires accurate sample drift correction in order to achieve good results. BaSDI implements a Bayesian statistical algorithm that estimate amount of the sample drift for every image frame from the raw dataset. The inference requires no fiducial marker but requires the assumption that the drift is mostly smooth over time. A detailed description of the statistical framework for this algorithm is published (see reference below).
Download
Download link for most recent version: BaSDI (Requires Matlab)
For tracking the development of the software you can check its code repository at GitHub.
Reference
Elmokadem A, Yu J, Optimal Drift Correction for Super-resolution Localization Microscopy with Bayesian Inference, Biophys J, in press(2015)
Octane
Super-resolution Imaging and Single Molecule Tracking Software
The Octane is a program we developed to facilitate works involved in super-resolution optical imaging (PALM, STORM, etc.). By providing an intuitive graphical user interface front end, we hope it can serve as a useful tool for a wide range of scientists, including experimental biologists as well as physicists. The program runs as a plugin of the (extremely versatile) ImageJ software, thus can be used on any image format that is supported by ImageJ.
Download
The current version is 1.5.1. To install, download the program and simply copy it under the plugin folder of your ImageJ installation. NOTE: This version requires a newer version of the math library.
Source code repository is online at github.com.
What's new in this version – Release Notes.
Requirement
- Java runtime 1.6. Earlier versions are not supported.
- ImageJ version 1.44 and up.
- Apache commons math library v3.0 or up. Download or get it from the Apache website. This should also be copied to ImageJ's plugin folder.
- Bean shell library: You only need this if you want to run your own script within the program.