http://imagej.273.s1.nabble.com/Is-manual-thresholding-methods-accepted-by-scientific-journals-tp5010814p5010863.html
thresholding topic together. I might be able to do this over Chistmas and
New Year. But I am already glad about any additional information in
> Hi everyone,
>
> I invite interested community members to contribute these valuable insights
> to the ImageJ wiki's "image processing principles" page at:
>
>
http://imagej.net/IP_Principles>
> I think it would make this information easier to find, benefiting many
> researchers.
>
> Regards,
> Curtis
>
> On Mon, Dec 8, 2014 at 1:51 PM, Adam Hughes <
[hidden email]>
> wrote:
>
> > 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:
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> > > inquiries:
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> > >
> > > --
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> >
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