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EMBL BioImage Data Analysis



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Timeline of @cmci_

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Impressive and swift moderation @helenajambor !
About 3 hours, 32 mins ago by: Kota Miura (@cmci_)

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RT @LuisDVerde: Now available as a #ggplot2 geom thanks to @naupakaz
About 3 hours, 35 mins ago by: Kota Miura (@cmci_)

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RT @helenajambor: awesome, within 2 days .@naupakaz made a code publicly available for anyone wanting to make the half-and-half-plot FYI @…
About 3 hours, 36 mins ago by: Kota Miura (@cmci_)

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RT @dgaboriau: Thread on data visualisation: ggplots, boxplots and violin plots
About 9 hours, 13 mins ago by: Kota Miura (@cmci_)

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RT @christlet: SUSHI from Valentin Nāgerl lab allows super-resolution microscopy of neuronal morphology by STED imaging of extracellular sp…
About 12 hours, 23 mins ago by: Kota Miura (@cmci_)

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RT @christlet: Our work with @HenriquesLab and @MercerLab on SQUIRREL highlighted on the @CNRS website (in French, and we even found a pun!…
About 12 hours, 25 mins ago by: Kota Miura (@cmci_)
Algorithm FRAP Fiji ImageJ ImageJ Plugin ImageJ Plugin 3Dviewer Imaris Java Javascript Python R bias blog dokuwiki fiji google imagej java libraries matlab meetings neubias news papers python references software webadmin

CMCI weblog

IJ plugin development

I added a small article about setting up Eclipse Run/Debug configuration in Eclipse.

4D registration

I didn't know this plugin made in 2005 for registration of 4D:

PoorMan3DReg A poor man's 3D registration plugin for aligning dynamic 3D volumes.

I only went to the bottom figurem but looks like a good plugin for 4D registration. I will check further. The author seems to be reserved from making it too famous because of other registration plugins available in ImageJ. I think, contrary to the authors view, such 4D availability has not been around.


QuickPALM: 3D real-time photoactivation nanoscopy image processing in ImageJ Nature Methods 7, 339 - 340 (2010) doi:10.1038/nmeth0510-339

PALM and STORM reconstruction algorithms usually rely on 'fitting' Gaussian kernels to detected diffraction-limited spots. Although they permit high-accuracy localizations, these iterative methods can require up to several hours of processing time. We have developed a high-speed reconstruction algorithm that uses the classical Högbom 'CLEAN' method8 for spot finding, followed by a modified center of mass algorithm to compute the spot position and parameters defining spot shape along the horizontal axes.

I have not yet tried this, but seems to be very usable also for simple cases that needs to find spots. Plugin could be downloaded from

Photobleaching Correction -3D time series

There are several IJ tools available for 2D time series bleaching correction, but seems not with 3D:

2D-t tools:

    • Phair's double normalization method. Dependent on ratio, similar to the above, but can specify reference area (if my understanding is correct).
    • proposes two ways,
      1. conceptually similar to above two: estimate the correction ratio. But this is done by fitting exponential decay curve and use decay parameter.
        1. <jsm> I_c(t) = I(t) / exp^{-\tau t}</jsm>
      2. use “enhance contrast”. framewise Histogram streching.

Among these, exponential decay method is theoretically clean (but in practice, timeseries are not teoretical…).

Anycase, there should be 3D-t bleach correction tool (and I need it NOW). I might make some quick solution using two methods, one using division of first frame and the other with exponential fitting.

By the way, bleaching corrected images cannot basically be used for intensity quantification (FRAP, on the other hands, correct bleaching after measuring the raw image). If you are analyzing shapes or positions, no problem for quantification.

here is the “ratio” version:

macro "Bleach Corection 3D-t by ratio"{
	run("Duplicate...", "title=bleach_corrected duplicate");
	getDimensions(width, height, channels, slices, frames);
	if (frames == 1) {
		uslices = getNumber("how many z slices/timepoint?", 1);
		if ((slices%uslices) !=0) exit("that slice number dows not match with the current stack");
		frames = slices / uslices;
	tIntA = newArray(frames);
	for(i=0; i<frames; i++){
		startf = (i*slices)+1;
		endf = (i+1)*slices;
		op ="start="+startf+" stop="+endf+" projection=[Sum Slices]";
		run("Z Project...", op);
		getRawStatistics(nPixels, mean);
		if (i==0) tIntA[i] = mean;
		else tIntA[i] = mean/tIntA[0];
	setBatchMode("exit and display");
	tIntA[0] =1;
	for(i=0; i<frames; i++){
		for(j=0; j<slices; j++){
			curframe = i*slices + j+1;
			//print("frame"+curframe + " factor" + tIntA[i]);
			op = "value="+tIntA[i]+" slice";
			run("Divide...", op);
		print("time point:"+i+1 + "  factor" + tIntA[i]);

Before Correction (each row is a time point, with 8 z-slices)
 Before each row is a time point Average intensity along stack slices. 5 peaks corresponds to 5 time points. Before

After Correction
After After

DeadEasy Caspase ImageJ plugin

There are often cases I see that ImageJ plugins that seems to be very useful is not linked to ImageJ plugin site. Here is one case, I just happened to find.

DeadEasy Caspase ImageJ plugin

“Here, we present a method based on image filtering and mathematical morphology techniques, to count automatically the number of dying cells in intact fruit-fly embryos. We call the resulting programme DeadEasy Caspase. It has been validated for Drosophila and we present examples of its power to address biological questions. Quantification is automatic, accurate, objective, and very fast.”

description is

entry generator

Weblog Archive

blogtng/blogtop.txt · Last modified: 2016/05/24 05:46 (external edit)