http://imagej.273.s1.nabble.com/help-with-3D-segmentation-tp5014798p5015007.html
> The I have to close it and start again. I have not been able to
Help menu, in the past in some situations. But I also do not know what
> This last week I focused mostly on the 3D-watershed approach.
> Updates are interleaved below.
>
> On 08/11/2015 9:10 AM, Aryeh Weiss wrote:
>
>> Further on my adventures using the DoG function:
>>
>> I mentioned that the DoGPeaks function appears to find minima as well as
>> maxima. I decided to go to the Difference of Gaussian function
>>
>>
http://javadoc.imagej.net/Fiji/mpicbg/imglib/algorithm/scalespace/DifferenceOfGaussian.html#DifferenceOfGaussian(mpicbg.imglib.image.Image,%20mpicbg.imglib.image.ImageFactory,%20mpicbg.imglib.function.Converter,%20mpicbg.imglib.outofbounds.OutOfBoundsStrategyFactory,%20double[],%20double[],%20B,%20B)
>>
>> Here I see that it explicitly states that it extracts local minima and
>> maxima of a certain size. I wonder if I can get only the maxima fro this
>> function.
>>
>>
> Jan Eglinger posted the answer to this. Here is what he wrote:
> -----
> You have to go one step further: the `DifferenceOfGaussian` class has a
> `findPeaks` method that gives you a list of `DifferenceOfGaussianPeak`s.
>
> In its Javadoc [1] you find the methods:
> boolean isMin()
> boolean isMax()
> that you can use to differentiate between minima and maxima.
>
> I modified your script to only get the maxima:
>
https://gist.github.com/imagejan/7cc228b9a5e57cce44a0>
> Here's what I changed:
>
https://gist.github.com/imagejan/7cc228b9a5e57cce44a0/revisions> -----
>
> This enables the threshold to be set lower, without picking up the minima.
>
> Among its parameters are Converter, OutOfBoundsStrategy (among others).
>> I did not understand how to use these classes, so I wonder if someone on
>> this list can tell me how to use them, or even better, point me at the
>> documentation that might help (the class definitions in javadoc were not
>> enough for me).
>>
>>
> Jan also added the following:
> -----
> Albert's excellent tutorials unfortunately make use of the now-deprecated
> ImgLib1 code that was superseded by ImgLib2 [2].
>
> Jean-Yves Tinevez's TrackMate plugin is a great example how to use
> ImgLib2. It's `LogDetector` [3] and `DogDetector` [4] classes show how to
> detect spots using ImgLib2 implementations of LoG and DoG.
>
> I added a script to the ImageJ wiki illustrating how to use `DogDetector`
> from a Python script:
>
http://imagej.net/Jython_Scripting#Find_peaks_in_a_3D_image> -----
>
> I did not have a chance to try this, because I focused on the watershed
> approach (see comments below).
>
>
> Thank in advance.
>> --aryeh
>>
>>
>> On 05/11/2015 12:58 PM, Aryeh Weiss wrote:
>>
>>> This is an update on the problem I posted.
>>>
>>> First, thanks to the people who replies with suggestions. From these, I
>>> am now following up on two of them.
>>>
>>> 1. Albert Cardona suggested trying the 3D (actually multi-D) difference
>>> of Gaussians (DoG) filter. This looks promising, plus it introduced me
>>> to imglib. It works great on isolated cells (actually, everything works
>>> great on isolated cells) and seems reasonable for my image, but still
>>> needs work. because I have such a dense collection of nuclei, it
>>> sometimes detects dark blobs, which are actually an approximately
>>> cell-sized island within a sea of nuclei.
>>> Albert's sample code displays the result in 3D using the 3D viewer.
>>> Unfortunately, java3D does not work in java 1.8, and in Java 1.6, while
>>> the java3D test works, the code that generates the 3D view fails -- that
>>> will go into a separate thread.
>>> The net result is that I can mark the centers of the nuclei (which is
>>> why it look promising) but I cannot visualize it in 3D to see if it
>>> really looks right.
>>>
>>> 2. Thomas Boudier suggest some 3D bandpass filtering followed by a
>>> seeded watershed. I will work more on finding the right parameters for
>>> this approach after I finish with the DoG approach. One problem is
>>> that the resolution in Z is much lower that in X-Y. As a result, the
>>> watershed does not separate the nuclei along Z. It is not clear to me
>>> why the DoG does. However, I still need to learn to to use the output
>>> of the watershed -- I may not be interpreting the output correctly.
>>>
>>>
> I found that I got a very good segmentation (ie, marking of the nuclei)
> by using the watershed in the droplet finder plugins. Or more accurately,
> using the droplet finder workflow which is run by Plugins>Droplet
> Finder>DF_Filterstacker
>
> The macro recorder enabled me to script it.
>
> // The code to get the file and parse
> // the filename and image title has been left out.
>
> run("32-bit"); // DF-Bandpass wants 32 bit input
>
> // The median, rolling ball filter, and Gaussian blur
> // greatly improved the accuracy
> run("Median...", "radius=2 stack");
> run("Subtract Background...", "rolling=50 sliding stack");
> run("Gaussian Blur...", "sigma=1.5 scaled stack");
>
> // macro recorder created this. This is a long command, so if you
> // cut and paste, you may have to reassemble it
> run("DF_Filterstacker", "maximal_feature_size=15 minimal_feature_size=7
> z/x_aspect_ratio=5.000 radius_x=2 radius_y=2 radius_z=2 invert
> image=[BP_"+dupTitle+"] mask=[WS_BP_"+dupTitle+"] area_threshold=0.300
> maximum_threshold=0.3 connect_threshold=0.80 slice=5");
> IJ.renameResults("Results");
> IJ.renameResults("Particle Results");
>
>
> This script produces a table called "Particle Results", with (among other
> things) the X, Y, and Z coordinates of the objects.
>
> I recently got access to Imaris (basic) with a spot detector. It does a
>>> decent job, but since I don't have it on my computer, I plan to solve
>>> this with ImageJ.
>>>
>>> When I have something that works and that I can visualize, I will post
>>> it for the benefit of other who, like me, have to muddle their way from
>>> 2D to 3D.
>>>
>>>
> In order to visualize the results, I have a python script to read the
> Particle Results table and produce a set of point ROIs that can mark the
> original image.
> The i can flatten the image and display this with the 3D Viewer (Java 1.6
> only).
>
> """
> This script converts the output of the droplet finder plugin (Paticle
> Results)
> to a set of point ROIs whcih can be used to mark the drops (in my case,
> nuclei)
> on the original image.
>
> Input: Particle Results table from Droplet Finder
> Output: An ROI Manager populated with point ROIs whose coordinates
> correspond to the list in the table.
>
> Bugs to fix: If the ROI Manager is open when the program is run, rm.addRoi
> fails with a null pointer error.
> """
>
> # Does not require auto-import
>
> from ij import IJ, WindowManager
> from ij.plugin.frame import RoiManager
> from ij.measure import ResultsTable
> from ij.gui import PointRoi
>
> dfTitle = "Particle Results"
> IJ.renameResults(dfTitle , "Results")
>
> rt = ResultsTable.getResultsTable()
>
> pos = []
>
> for i in range(rt.size()):
> pos.append([rt.getValue("position_x",i), rt.getValue("position_y",
> i), rt.getValue("position_z", i)])
>
> for i in range(rt.size()):
> print pos[i];
>
> print len(pos)
>
> dfTitle = "Particle Results"
> IJ.renameResults( "Results" , dfTitle )
>
> # I do not know why this does not prevent the null pointer error
> # which I get if the ROI Manager is open when this script is run
> if "ROI Manager" in WindowManager.getImageTitles():
> WindowManager.getWindow("ROI Manager").close()
>
> rm = RoiManager()
> rm.show()
>
> for p in pos:
> cellRoi = PointRoi(int(p[0]),int(p[1]))
> cellRoi.setPosition(int(p[2]))
> rm.addRoi(cellRoi)
>
> rm.runCommand("Show All without labels")
>
> # end of script
>
> I actually created two images -- one is the original, and the second is a
> blank image with the point ROIs flattened into it. Then I overlay them with
> the 3D viewer and increase transparency of the data to see the marks. What
> I really want to do is make the nuclei into surfaces and then "peel" them
> away to reveal the marks. I have not yet figured that out, and the surface
> rendering takes a really long time.
>
> One problem I have is that sooner or later, the 3D Viewer menus get grayed
> out (though mouse clicks in the 3D Viewer window still work).
> The I have to close it and start again. I have not been able to associate
> this with a reproducible set of actions.
>
>
> I hope this is useful
>
> --aryeh
>
> --
> Aryeh Weiss
> Faculty of Engineering
> Bar Ilan University
> Ramat Gan 52900 Israel
>
> Ph: 972-3-5317638
> FAX: 972-3-7384051
>
> --
> ImageJ mailing list:
http://imagej.nih.gov/ij/list.html>