http://imagej.273.s1.nabble.com/Is-manual-thresholding-methods-accepted-by-scientific-journals-tp5010814p5010849.html
I think that Jan hit it right on the head. I just want to add that if you
you want, then your method is sound. For images with objects significantly
works great. You could use Fiji's thresholding plugin to test 25 methods
image nicely. If that's the case, then you could switch, or at least in
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
> 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!
> >
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http://imagej.nih.gov/ij/list.html> >
>
>
>
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