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<p>Dear Marin,</p>
<p>thank you for the reminder, since I had certainly forgotten. What
you call a contrast reversal is most likely a nomenclature issue.
If you have two images, I_observed and I_ideal, and the
relationship between the two is simply a multiplicative term</p>
<p>I_observed = H I_ideal</p>
<p>exactly the same relationship is described by the relationship
(assuming there are no zeros)<br>
</p>
<p>F I_observed = I_ideal</p>
<p>The relationship between F and H is F=1/H, which implies a
contrast reversion. I have explictly avoided G to denote any of
these two images (F or H) to avoid confusions. Which of the two is
called "gain" is a matter of definition. I prefer describing H as
the gain, but I understand other people may prefer the other
option.</p>
<p>Regarding R^2, it is true I did not define it assuming it was
general knowledge. It is a very standard statistical measure of
quality called the coefficient of determination
(<a class="moz-txt-link-freetext" href="https://en.wikipedia.org/wiki/Coefficient_of_determination">https://en.wikipedia.org/wiki/Coefficient_of_determination</a>), and
it expresses the fraction of the original variance explained by a
model. If R^2=1, the model has totally explained the original
variance, while if R^2=0, the model explains the same variance as
the mean of the observations, which is the simplest, sensible
model we could have.</p>
<p>Cheers, Carlos Oscar<br>
</p>
<div class="moz-cite-prefix">El 04/12/2018 a las 21:48, Marin van
Heel escribió:<br>
</div>
<blockquote type="cite"
cite="mid:CAHgnXPXmP2zUNa1J_p0v4JZ9onvVsQ2MpANtdLLpzw4Hy1A3DA@mail.gmail.com">
<meta http-equiv="content-type" content="text/html; charset=UTF-8">
<div dir="ltr">
<div dir="ltr">
<div>Hi Carlos Oscar! <br>
</div>
<div>I just remembered I had posed a question about your
camera normalisation paper (
<a href="https://www.ncbi.nlm.nih.gov/pubmed/29551714"
rel="noreferrer" target="_blank" moz-do-not-send="true">https://www.ncbi.nlm.nih.gov/pubmed/29551714</a>.)
on this site some two months ago, in which you critisized
our 2015 camera normalisation paper (<a
href="https://www.nature.com/articles/srep10317"
moz-do-not-send="true">https://www.nature.com/articles/srep10317</a>).
Did you already respond to my question and I missed your
answer? <br>
</div>
<div><br>
</div>
<div>Cheers</div>
<div>Marin<br>
</div>
<div><br>
</div>
<div>My question was:</div>
<div><br>
</div>
<div>QUOTE:<br>
</div>
<div>
What you call our "gain image" is - apart from an erroneous
contrast reversal - actually more similar to the "official"
gain image in your Fig 1 than does the one generated with
your proposed algorithm. I would be interested in knowing
what the R2 turns out to be after you correct the contrast
reversal since it visually is better than yours. It would be
nice if you could respond to this mailing including that
information!? By the way how exactly is this R2 metric
defined (I could not find it anywhere in the paper)? <br>
</div>
<div>END QUOTE<br>
</div>
</div>
</div>
<br>
<div class="gmail_quote">
<div dir="ltr">On Tue, Oct 2, 2018 at 10:21 PM Marin van Heel
<<a href="mailto:marin.vanheel@googlemail.com"
moz-do-not-send="true">marin.vanheel@googlemail.com</a>>
wrote:<br>
</div>
<blockquote class="gmail_quote" style="margin:0px 0px 0px
0.8ex;border-left:1px solid rgb(204,204,204);padding-left:1ex">
<div bgcolor="#FFFFFF">
<div class="gmail-m_-1407481537373201022moz-cite-prefix">Dear
Carlos Oscar and Dimitry,<br>
<br>
Unfortunately, you seem to have missed the point of our
Afanasyev 2015 paper. Our paper does not try to duplicate
the "experimentally determined Gain image" but tries to
normalize the signal from each pixel to the same average
and the same standard deviation at the exposure and
contrast level that the data set was recorded. Our
approach typically improves significantly on standard "<i>a
priori</i>" flat field/gain corrections.<br>
<br>
We are not directly interested in generic "gain images" as
such and we certainly do not generate "gain images" that
have an inverted contrast when compared to the other ones
you have in Figure #1 of your paper. Your comments on our
methods are thus not appropriate: "<i>T</i><i>o the best
of our knowledge, the only article that addresses a
similar problem is that of Afanasyev </i><i>et al.</i><i>
(2015). In their work, they assimilate the gain of the
camera to the standard deviation of each pixel over a
large number of movies, and they prove this is a
successful way of identifying dead pixels. However, our
results show that this approach does not provide a
consistent gain estimation (Fig. 1)</i>."<br>
<br>
What you call our "gain image" is - apart from an
erroneous contrast reversal - actually more similar to the
"official" gain image in your Fig 1 than does the one
generated with your proposed algorithm. I would be
interested in knowing what the R2 turns out to be after
you correct the contrast reversal since it visually is
better than yours. It would be nice if you could respond
to this mailing including that information!? By the way
how exactly is this R2 metric defined (I could not find it
anywhere in the paper)? <br>
<br>
I would want to suggest you and your colleagues to use the
FRC metric to prove that your approach does indeed remove
the influence of the various patterns of your detectors
exhibits.<br>
<br>
My two cents<br>
<br>
Marin<br>
<br>
=====================<br>
<br>
<br>
On 02/10/2018 15:19, Carlos Oscar Sorzano wrote:<br>
</div>
<blockquote type="cite">By the way, in our article we
compared both methods (ours and Marin). <br>
<br>
Kind regards, Carlos Oscar <br>
<br>
<br>
On 01/10/2018 21:23, Marin van Heel wrote: <br>
<blockquote type="cite">Dear Da, <br>
<br>
In IMAGIC-4D you can perform the necessary camera
correction! (<a
class="gmail-m_-1407481537373201022moz-txt-link-freetext"
href="https://www.nature.com/articles/srep10317"
target="_blank" moz-do-not-send="true">https://www.nature.com/articles/srep10317</a>).
It does it better than any manufactures correction and
improves the data significantly even when performed
after using the standard gain correction. <br>
<br>
Cheers, <br>
<br>
Marin <br>
<br>
<br>
===================================================== <br>
<br>
On 01/10/2018 15:36, Da Cui wrote: <br>
<blockquote type="cite">Hi all, <br>
The gain reference image for one dataset was
missing by accident. In order to achieve a more
accurate motioncor result, does anyone have idea about
how to generate a gain reference image from the
dataset (around 3k movies)? <br>
Thank you so much for your help!!! <br>
---Da <br>
<br>
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</p>
<pre class="gmail-m_-1407481537373201022moz-signature" cols="72">--
==============================================================
Prof Dr Ir Marin van Heel
Laboratório Nacional de Nanotecnologia - LNNano
CNPEM/LNNano, Campinas, Brazil
tel: +55-19-3518-2316
mobile +55-19-983455450 (current)
mobile +55-19-981809332
(041-19-981809332 TIM)
Skype: Marin.van.Heel
email: marin.vanheel(A_T)<a href="http://gmail.com" target="_blank" moz-do-not-send="true">gmail.com</a>
marin.vanheel(A_T)<a href="http://lnnano.cnpem.br" target="_blank" moz-do-not-send="true">lnnano.cnpem.br</a>
and: mvh.office(A_T)<a href="http://gmail.com" target="_blank" moz-do-not-send="true">gmail.com</a>
--------------------------------------------------
Emeritus Professor of Cryo-EM Data Processing
Leiden University
Mobile NL: +31(0)652736618 (ALWAYS ACTIVE SMS)
--------------------------------------------------
Emeritus Professor of Structural Biology
Imperial College London
Faculty of Natural Sciences
email: m.vanheel(A_T)<a href="http://imperial.ac.uk" target="_blank" moz-do-not-send="true">imperial.ac.uk</a>
--------------------------------------------------
I receive many emails per day and, although I try,
there is no guarantee that I will actually read each incoming email. </pre>
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</blockquote>
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</blockquote>
<pre class="moz-signature" cols="72">--
------------------------------------------------------------------------
Carlos Oscar Sánchez Sorzano e-mail: <a class="moz-txt-link-abbreviated" href="mailto:coss@cnb.csic.es">coss@cnb.csic.es</a>
Biocomputing unit <a class="moz-txt-link-freetext" href="http://i2pc.es/coss">http://i2pc.es/coss</a>
National Center of Biotechnology (CSIC)
c/Darwin, 3
Campus Universidad Autónoma (Cantoblanco) Tlf: 34-91-585 4510
28049 MADRID (SPAIN) Fax: 34-91-585 4506
------------------------------------------------------------------------
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