FLIMJ Ops are a collection of FLIM analysis ops based on FLIMLib. It extends the single-transient fitting functions in FLIMLib to dataset-level fitting ops. Currently supported fitting ops include: RLD, MLA, Global, Phasor, and single-component Bayesian.
Besides curve fitting, FLIMJ Ops also provide a variety of pre-processing options such as pixel binning, intensity thresholding, ROI masking as well as post-processing utility ops for e.g. calculating τm (mean lifetime), Ai% (fractional contribution) and pseudocoloring the result with LUT.
Most of the FLIMJ fitting ops supports multithreading. Fitting a dataset like the one below takes no more than a couple of seconds!
Add https://sites.imagej.net/FLIMJ/ update site on the Update manager
Open test2.sdt in Fiji. Execute in Script Editor as Groovy:
# @ImageJ ij
# @ImgPlus img
op = ij.op()
// set up parameters
import flimlib.flimj.FitParams
param = new FitParams()
param.transMap = img; // input 3-dimensional (x, y, t) dataset
param.xInc= 0.040 // time difference between bins (ns)
param.ltAxis = 2 // time bins lay along axis #2
// op call
fittedImg = op.run("flim.fitLMA", param).paramMap
// display each parameter
zImg = op.transform().hyperSliceView(fittedImg, param.ltAxis, 0)
AImg = op.transform().hyperSliceView(fittedImg, param.ltAxis, 1)
tauImg = op.transform().hyperSliceView(fittedImg, param.ltAxis, 2)
ij.ui().show("z", zImg)
ij.ui().show("A", AImg)
ij.ui().show("tau", tauImg)
Output (z in [-1, 1], A in [0, 4], tau in[0, 3]):
See more examples in Demo.ipynb and groovy.md.
To depend on FLIMJ Ops, copy the following to your pom.xml:
<properties>
<flimj-ops.version>2.1.1</flimj-ops.version>
</properties>
<dependencies>
<!-- FLIMJ Ops dependency -->
<dependency>
<groupId>flimlib</groupId>
<artifactId>flimj-ops</artifactId>
<version>${flimj-ops.version}</version>
</dependency>
</dependencies>Comming soon...