http://imagej.273.s1.nabble.com/Is-manual-thresholding-methods-accepted-by-scientific-journals-tp5010814p5010852.html
> I think that Jan hit it right on the head. I just want to add that if you
> don't need to automate the analysis, and you're getting segmentations that
> you want, then your method is sound. For images with objects significantly
> brighter than the background and of uniform brightness, manual thresholding
> works great. You could use Fiji's thresholding plugin to test 25 methods
> in tandem, and maybe you'll find that several of them also segment your
> image nicely. If that's the case, then you could switch, or at least in
> the paper mention the other methods that worked.
>
> I just released a preprint on this exact subject when applied to SEM images
> of nanoparticles, and I think it's relevant enough that I'm going to share
> it shamelessly. The conclusions that Jan reached are basically the same as
> what I said in the Thresholding section of my paper. The paper also goes
> on to look at other types of segmentation, and how to classify objects once
> you've segmented them.
>
>
https://peerj.com/preprints/671/>
> On Mon, Dec 8, 2014 at 8:39 AM, BioVoxxel <
[hidden email]>
> wrote:
>
> > Dear Anders, dear all,
> >
> > potentially I can also add some personal opinion to this indeed
> interesting
> > and moreover important question, since many people face the problem on
> > deciding for a "proper" method (however you want to define this) as I
> often
> > figure out talking to students about these topics.
> >
> > Your question actually has two parts which I think need to be addressed
> > (thresholding and co-ocalization):
> >
> > 1.) Thresholding:
> >
> > Generally, I would not (never) decide on a method just because you will
> > "get away with it" or because a specific journal would accept it. Just
> > because it will be accepted by a few people does still not necessarily
> mean
> > that it is a suitable or appropriate analysis method. That said, here
> some
> > considerations.
> >
> > As Dimiter pointed it out already.... thresholding is not easy and
> > sometimes might not lead you to a satisfyingly accurate result.
> > Nevertheless, I had so far mostly good experiences using automatic
> > thresholding methods. But in many cases you will only get a good feature
> > extraction if you pre-process the images. This in turn might/will lead to
> > an alteration of feature outlines, forms and sizes. So, you first need to
> > figure out such processing steps and check if this would still be
> > acceptable regarding the features you need to extract. A possible help in
> > deciding for a suitable filtering and thresholding might be the "Filter
> > Check" as well as the "Threshold Check" (using the available auto
> > thresholds in ImageJ and Fiji) from the BioVoxxel Toolbox (
> >
http://fiji.sc/BioVoxxel_Toolbox).
> > I further agree with Michael Schell that it is worth investing the time
> in
> > finding suitable auto-thresholds if possible, or one of the methods
> Dimiter
> > mentioned. Because you reduce user bias and improve the extraction
> result.
> >
> > Manual thresholds are not really suitable if you try to analyze a bigger
> > set of data for several reasons.
> > In terms of comparability you need to apply the same threshold to all
> > images in your experiment to keep the user bias at least a little lower
> > (besides your decision for the initial threshold). Due to natural
> > variability in your images a manual threshold with one or even two (an
> > upper and lower) cut-off value(s) will not work on a full set of data
> under
> > most conditions. An automatic threshold might also fail to achieve this
> but
> > since those algorithms consider the image histogram they account for
> those
> > variabilities and the chance to find suitable cut-offs is way higher.
> > If you manually threshold each image with different cut-off values you
> > actually loose comparability in your experiment completely due to massive
> > user bias.
> >
> > Another problem, since you where talking about separating signal from
> > no-signal, is identifying a suitable separation of those two parts. Your
> > background in a intensity based fluorescent image is in most cases very
> > dark. Our vision unfortunately is very prone to mis-interpret different
> > intensities which is getting especially difficult the darker those are.
> > Additionally, we perceive different colors with different visual
> > sensitivities. Thus, it is also important if you look at a grayscale
> image
> > (with a gray LUT) or at the same image in one of different false colors.
> > Gray is always preferable in this context because you will see
> differences
> > best.
> >
> > Nevertheless, manual thresholding is very subjective and should be
> avoided
> > whenever possible. Even without those it is already difficult enough to
> > achieve objective analyses for many studies (my opinion).
> >
> >
> > 2.) Co-localization
> > If I got it correctly, your initial aim is a co-localization study. In
> this
> > context, I would not rely only on a pixel based overlap determination.
> This
> > might give you a hint and is partially used during object-based
> > co-localization studies. Nevertheless, you should consider additional
> > parameters like resolution limit and your actual image resolution and the
> > intensity distribution in your images/features. Therefore, I would pay
> > attention first to a proper imaging setup, with a good nyquist-sampled
> > image and usage of the full dynamic range (besides other parameters). To
> > this end the following paper might be helpful:
> >
> > Jennifer C. Waters, J Cell Biol. Jun 29, 2009; 185(7): 1135-1148.
> Accuracy
> > and precision in quantitative fluorescence microscopy.
> >
> > Analysis wise there are two very excellent tools available in Fiji which
> is
> > the Coloc2 and the JACoP. The latter implements two object-based methods
> > including a thresholding. In this context it might also be a possibility
> to
> > use binary images as result of an auto-thresholding and mask your
> original
> > images with them (e.g. with >Edit >Paste Control or the >Process > Image
> > Calculator). This is similar in applying a ROI as possible in the Coloc2
> > plugin.
> > As a suggestion for co-localization studies... I would not rely on a
> single
> > output method only, but rather combine several suitable ones as is
> possible
> > in Coloc2 and JACoP to get a better confidentiality about a potential
> > co-localization.
> >
> > So, to not create more confusion here some interesting reading and
> > important literature regarding co-localization:
> >
> > There was an interesting discussion about different co-localization
> > analyses on the list a few month ago with some papers suggested already (
> >
> >
>
https://list.nih.gov/cgi-bin/wa.exe?A2=ind1403&L=IMAGEJ&P=R31566&1=IMAGEJ&9=A&I=-3&J=on&d=No+Match%3BMatch%3BMatches&z=4> > )
> >
> > Recommendable, especially if you use the JACoP tool: Bolte and
> Cordelieres,
> > J Microsc. 2006 Dec;224(Pt 3):213-32. A guided tour into subcellular
> > colocalization analysis in light microscopy
> >
> > Furthermore: Dunn et al. Am J Physiol Cell Physiol. 2011
> > Apr;300(4):C723-42. A practical guide to evaluating colocalization in
> > biological microscopy
> >
> > There are many more papers regarding the topic e.g. from Elise Stanley's
> > lab, Ingela Parmryd and Jeremy Adler. And many more I might not be aware
> > of.
> >
> > So, in the worst case I misunderstood your question and overloaded you
> with
> > unnecessary answers (but they might be helpful to others). In the best
> case
> > you have a lot of reading suggestions and potentially a clearer picture
> on
> > how you want to start your analysis.
> >
> > Kind regards,
> > Jan
> >
> >
> >
> > 2014-12-06 16:00 GMT+01:00 Anders Lunde <
[hidden email]>:
> >
> > > Dear mailing list,
> > >
> > > I have developed a nice macro for identifying colocalized signals for
> > > z-stack confocal images with multiple channels/colors. However, my
> > > advisor/professor has now come to question my method for setting a
> > > threshold for signal/no-signal in the infividual channels.
> > >
> > > My manual method has been to simply raise the threshold above what I
> > > relatively confidently can see is background, like large areas with no
> > > apparent staining. The reason I did it manually is because when I
> played
> > > around with the automatic thresholding methods in ImageJ I decided that
> > > they were not any better than manual and could be subject to mistakes.
> > >
> > > My supervisor now feels that this sounds too subjective and would not
> > look
> > > good in a paper. He therefore asked me to try to find a way that was
> more
> > > guided e.g. by the histogram or something, anything that is less
> > subjective
> > > (not sure if he is worried about accuracy or how it sounds in a paper).
> > >
> > > What is the current standard for this kind of analysis in scientific
> > > journals, in particular with regards to the acceptability of manual
> > > thresholding of immunofluorescent brain sections stained with various
> > > antibodies (and nuclear markers and neuron trancers)? Is there a
> > preference
> > > for automated, manual or some hybrid methods? Could I "get-away" with
> > > something like this: "Thresholds were set manually at a level that
> > > excluded most pixels in assumed background areas. Inspection of the
> > > assigned threshold level in the ImageJ intensity histogram showed that
> > the
> > > thresholds were set at where the main peak (background pixels) started
> to
> > > or had reached a minimum value."
> > >
> > > Image set that Im working on:
> > >
> > > I am working with images of brain sections with 4 colors/channels:
> > nuclear
> > > stain, two immunofluorescence staining for transciption factors
> (nuclear
> > > localization), and a retrograde nerve cell staning (nuclear + cytoplasm
> > > staining).
> > >
> > > Greateful for any advice!
> > >
> > > --
> > > ImageJ mailing list:
http://imagej.nih.gov/ij/list.html> > >
> >
> >
> >
> > --
> >
> > CEO: Dr. rer. nat. Jan Brocher
> > phone: +49 (0)6234 917 03 39
> > mobile: +49 (0)176 705 746 81
> > e-mail:
[hidden email]
> > info:
[hidden email]
> > inquiries:
[hidden email]
> > web: www.biovoxxel.de
> >
> > --
> > ImageJ mailing list:
http://imagej.nih.gov/ij/list.html> >
>
> --
> ImageJ mailing list:
http://imagej.nih.gov/ij/list.html>