PixBleach

Pixelwise analysis of bleach rate in time-lapse images

Daniel Sage at the Biomedical Image Group (BIG), EPFL, Switzerland
Daniel Wüstner at the Department of Biochemistry and Molecular Biology, University of South Denmark, Denmark

Outline

PixBleach is a ImageJ plugin which fits fluorescence photobleaching decay in the temporal sequence for every pixels. Three common decal models are available: the mono-exponential, the bi-exponential, and the stretched exponential. Bleach rate (time-constant) and other fitted parameters can be visualized as 32-bit image and exploited for further analysis.

Reference

Daniel Wüstner, Ane Landt Larsen, Nils J. Faergeman, Jonathan R. Brewer and Daniel Sage "Selective visualization of fluorescent sterols in Caenorhabditis elegans by bleach-rate based image segmentation" Traffic, Published Online: 12 Jan 2010.

Software

Specification

PixBleach is a program to analyse the bleach rate of sequence of images at the pixelwise level. PixBleach tries to fit an exponential decay model (3 models are available) from the data. Better results are obtained when the number of frames is large enough, when the data evoluted as the model, and when the plateau of the exponential is in the data.

Mono-exponential model	Bi-exponential model	Stretched-exponential model

This model contains 3 parameters to optimize, B the background level, A the amplitude of the exponential and τ the time-constant which characterizes the lifetime of the fluorophore.	This model contains 5 parameters to optimize, B the background level, A₁ and A₂ the amplitudes of the exponential, τ₁ the short-lived time-constant and, τ₂ the long-lived time-constant.	This model contains 4 parameters to optimize, B the background level, A the amplitude of the exponential, τ the time-constant and, the heterogeneity factor h.

A preprocessing can be first applied to the data. It consists in two stages 1) a spatio-temporal Gaussian blur allows to denoise images and compensate the small variations of the sample and 2) a check of a minimal decay. This last step avoids to fit a decay model where there is evidence that the signal do not have any decay.

The iterative optimization (Marquardt-Levenberg) is controlled through to stopping parameters: the maximum number of iterations and the maximum chi-square. The background B can be optimized as free parameters or it could be set to a fixed value (e.g. 0) if your data do not have any background.

Post-processing can be acheived on request: computation the reconstruction sequence of images from the optimized parameters, computation of the RMSE (Root Mean Square Error), computation of the time-integrated emission (TiEm) including or not the background, and selection of "good" result based on thresholds of the time-constant and the chi-square.

A companion plugin PixBleach_Generate_Control allows to create synthetic sequence of images with some variations of the parameters.

ImageJ prerequirement

The software provided here is a plugin for ImageJ, a general purpose image-processing and image-analysis package. ImageJ has a public domain licence; it runs on several plateforms: Unix, Linux, Windows, and Mac OS X. It doesn't take more than a couple of minutes to install.

Download and install

Download PixBleach_.jar the ImageJ's plugin. The plugin consists in one single JAR file; place it into the "plugins" folder of ImageJ. Do not unzip the JAR file.

Screenshots

Enlarge the screenshot

Conditions of use

You are free to use this software for research purposes, but you should not redistribute it without our consent.
In addition, we expect you to include adequate citations and acknowledgments whenever you present or publish results that are based on it.