http://imagej.273.s1.nabble.com/Segmentation-Problem-tp5005009p5005084.html
> Hi Pascal,
>
>> Unfortunately I could not find mentioned attachments. I would be
>> curious to see your results. Would you mind sending them directly
>> again?
> Looks like the ImageJ list-serve strips many attachments. So I posted them
> at:
>
>
http://curtis.imagej.net/2013-10-07/trainable-weka.jpg>
http://curtis.imagej.net/2013-10-07/results.jpg>
>> I tried to follow the process and it seems to be really a good
>> approach. If I understood it correctly I will end up with the centre
>> of the cells.
> Right.
>
>> This is for sure enough to count them but I am not able to exclude
>> cells that don't fit the circularity criteria. Do you see a
>> possibility for that as well?
> Off the top of my head, I do not know the best way to reconcile the cell
> centers with the imperfect binary mask segmentation. To exclude based on
> circularity you would need to use Analyze Particles with a less messy
> binary mask. There may be a way to use the center points as the starting
> point for an operation that cleans up the mask, but I do not know what it
> is. Maybe someone else on this list has an idea.
>
>> Or is this already taken into account in the "ulitmate points"
>> function? I did not really understand what this function exactly does.
> The function is described here:
>
http://imagej.net/docs/guide/146-29.html#sub:Ultimate-Points>
> Sorry I cannot be of more assistance but I don't currently have the time to
> play with this analysis further right now.
>
> Regards,
> Curtis
>
>
> On Mon, Oct 7, 2013 at 5:00 AM, Pascal Lorentz <
[hidden email]>wrote:
>
>> Hi Curtis
>>
>> Thanks you very much for your advice and even the macro code.
>> Unfortunately I could not find mentioned attachments. I would be curious
>> to see your results. Would you mind sending them directly again?
>> I tried to follow the process and it seems to be really a good approach.
>> If I understood it correctly I will end up with the centre of the cells.
>> This is for sure enough to count them but I am not able to exclude cells
>> that don't fit the circularity criteria. Do you see a possibility for that
>> as well? Or is this already taken into account in the "ulitmate points"
>> function? I did not really understand what this function exactly does.
>>
>> Best regards
>>
>> Pascal
>>
>>
>> Am 03.10.2013 19:07, schrieb Curtis Rueden:
>>
>> Hi Michael & Pascal,
>>> Here is a potential approach that might work with some fine tuning:
>>>
>>> 1) Use Trainable Weka Segmentation to separate white cells from red cells
>>> from background.
>>>
>>> 2) From the white cells mask, clean up the mask as best as possible using
>>> morphological operations (e.g., Process > Binary > Open).
>>>
>>> 3) Since cells are blob-shaped (i.e., not too long and skinny), use
>>> Ultimate Points to compute cell centers from the Euclidean distance map.
>>>
>>> Here is a macro for steps 2 & 3:
>>>
>>> call("trainableSegmentation.**Weka_Segmentation.getResult");
>>> selectWindow("Classified image");
>>> setAutoThreshold("Default dark");
>>> setThreshold(0, 60); // threshold to white cells only
>>> run("Create Mask"); // create a mask of the white cells
>>> run("Open"); // clean up the mask a little
>>> run("Ultimate Points"); // compute segmentation centers from EDM
>>> setAutoThreshold("Default dark");
>>> setThreshold(30, 255); // threshold to only centers of radius 30+
>>> run("Create Selection");
>>> selectWindow("Classified image");
>>> close();
>>> selectWindow("mask");
>>> close();
>>> selectWindow("2151833.png"); // switch back to the original image
>>> run("Restore Selection"); // show cell centers overlaid on original data
>>>
>>> It assumes you have opened the data (2151833.png), then run Trainable Weka
>>> Segmentation to perform the initial segmentation (see attached
>>> trainable-weka.jpg).
>>>
>>> My initial results are not perfect (see attached results.jpg) but not bad.
>>> Further fiddling of the TWS classifier might improve matters. And once you
>>> have a really good classifier, you can fully automate the process for
>>> additional images; for details see:
>>>
http://fiji.sc/Trainable_Weka_**Segmentation#Macro_language_**>>> compatibility<
http://fiji.sc/Trainable_Weka_Segmentation#Macro_language_compatibility>
>>>
>>> Regards,
>>> Curtis
>>>
>>>
>>> On Thu, Oct 3, 2013 at 11:08 AM, Cammer, Michael <
>>>
[hidden email]
>>>
>>>> wrote:
>>>> I tried converting to HSV, media filtering, and segmenting each with
>>>> simple threshold or find edges / variance and use logical AND to narrow
>>>> to
>>>> the white cells. Isolating the white cells from everything else is simple
>>>> but I couldn't get the cells to separate from each other. If anybody
>>>> else
>>>> has a method, I'd be very interested to hear it too.
>>>>
>>>> But if she has fewer than 1000 or so cells, I'd just trace them.
>>>>
>>>> Regards,
>>>>
>>>> Michael
>>>>
>>>>
>>>>
>>>> -----Original Message-----
>>>> From: ImageJ Interest Group [mailto:
[hidden email]] On Behalf Of
>>>> Pascal Lorentz
>>>> Sent: Wednesday, October 02, 2013 7:54 AM
>>>> To:
[hidden email]
>>>> Subject: Segmentation Problem
>>>>
>>>> Dear List
>>>>
>>>> I got this Image of muscle fiber (Fiber-I white and Fiber-II red) from a
>>>> user:
>>>>
>>>>
http://www.picfront.org/d/91MZ>>>>
>>>> She wants to compare white versus red cells. She needs the percentage of
>>>> cells (white vs red) but only cells that have a circularity between 0.6
>>>> and
>>>> 0.8 should be considered. Truncated cells should be excludes as well. I
>>>> am
>>>> able to seperate white and red cells but just area wise. I would be glad
>>>> if
>>>> anybody as a good procedure to segment the single cells (if possible at
>>>> all).
>>>>
>>>> Thanks a lot for your help
>>>>
>>>> Pascal
>>>>
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>>
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