http://imagej.273.s1.nabble.com/Is-manual-thresholding-methods-accepted-by-scientific-journals-tp5010814p5010866.html
balance to be struck. One option would be to pursue the matter in the same
that could be links to discussion threads of this mailing list. Please let
> Hi Curtis,
>
> I take your invitation and will start to put some information about the
> 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
> advance, like publications regarding qualitative and quantitative
> assessment of thresholding qualities, etc.
> Since a well checked procedure for such a guideline is critical, once the
> basic information is setup I will drop a message to invite especially
> people involved in threshold development or image segmentation to take my
> basic text skeleton and further improve it via interactive discussions to
> finally end up with a feedback controlled guideline.
>
> cheers,
> Jan
>
> 2014-12-08 22:03 GMT+01:00 Curtis Rueden <
[hidden email]>:
>
> > 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
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> > > >
> > > > --
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> > >
> > > --
> > > ImageJ mailing list:
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> >
> > --
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>
>
>
> --
>
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> 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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