<div dir="ltr"><div dir="ltr"><div>Hi Carlos Oscar! <br></div><div></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">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">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">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">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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<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
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marin.vanheel(A_T)<a href="http://lnnano.cnpem.br" target="_blank">lnnano.cnpem.br</a>
and: mvh.office(A_T)<a href="http://gmail.com" target="_blank">gmail.com</a>
--------------------------------------------------
Emeritus Professor of Cryo-EM Data Processing
Leiden University
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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">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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