<div>Hi</div>The MultiScaleHessian Family of filters in ITK has a way of detecting plate-like structures (<a href="http://www.itk.org/Doxygen320/html/classitk_1_1HessianToObjectnessMeasureImageFilter.html#_details">http://www.itk.org/Doxygen320/html/classitk_1_1HessianToObjectnessMeasureImageFilter.html#_details</a>) so maybe the table can be detected as a high response to that kind of filter at the adequate scale. Give it a try to see if it works.<div>
The advantage is that no seed points would be required to to this.</div><div><br></div><div>HTH<br><br><div class="gmail_quote">2011/4/6 Lodron, Gerald <span dir="ltr"><<a href="mailto:Gerald.Lodron@joanneum.at">Gerald.Lodron@joanneum.at</a>></span><br>
<blockquote class="gmail_quote" style="margin:0 0 0 .8ex;border-left:1px #ccc solid;padding-left:1ex;">
<div bgcolor="#ffffff" text="#000000">
<div dir="ltr" align="left"> </div>
<div dir="ltr" align="left"><span>Hi</span></div>
<div dir="ltr" align="left"><span></span> </div>
<div dir="ltr" align="left"><span>I do not want to segment
heart, lung etc seperately, i want to segment human vs background/patient table,
especially for CT head data. </span></div>
<div dir="ltr" align="left"><span></span> </div>
<div dir="ltr" align="left"><span>I don't think that this
is a so special problem, in my opinion every time you want to register a ct
data set you need such a segmentation for excluding background so i think this a
very common task. I already wrote a segmentation algorithm based on the itk
region grower with seed points in the image center but i get sometimes problems
with the patient table (and i want it 100% automatic) so before i make
complicated algorithms i want to first ask the community.</span></div>
<div dir="ltr" align="left"><span></span> </div>
<div dir="ltr" align="left"><span>best regards</span></div>
<div dir="ltr" align="left">
<hr>
</div>
<div dir="ltr" align="left"><font face="Tahoma"><b>Von:</b>
<a href="mailto:insight-users-bounces@itk.org" target="_blank">insight-users-bounces@itk.org</a> [mailto:<a href="mailto:insight-users-bounces@itk.org" target="_blank">insight-users-bounces@itk.org</a>] <b>Im
Auftrag von </b>Jon Haitz Legarreta Gorroño<br><b>Gesendet:</b> Mittwoch, 6.
April 2011 10:48<br><b>An:</b> <a href="mailto:insight-users@itk.org" target="_blank">insight-users@itk.org</a><br><b>Betreff:</b> Re:
[Insight-users] Segmentation of CT data<br></font><br></div><div><div></div><div class="h5">
<div></div><br>Dear Gerald,<br>the segmentation problem is highly specific and
depends on the tissue you wish to segment. If you want to segment every
organ/tissue in a CT dataset, I would dare to say that you will need to look for
the most suitable algorithm for each of them.<br>The ITK Software Guide presents
an interesting range of segmentation algorithms that you can use your starting
point.<br><br>So there is not a one-for-all, all-purpose algorithm. Segmenting
all tissues you may have in a certain volume can be hard work.<br> <br>That
said, I haven't had the opportunity to approach the patient-table removal
problem. But if your requirements allow for some user interaction, you can
imagine the user selecting a volumen of interest that excludes the table (may be
too optimistic), or the user picking some seed points on the tissues you want to
segment and the developping some of your algorithms around these points (this
would be a typical starting point for the region growing algorithm you used).
Granting the user some control when overflows occur could also be helpful.
<br>That way, in principle, you could avoid the problem. But I agree that it may
not be that easy.<br><br>Best regards,<br>JON HAITZ<br><br><br><br><br>El
05/04/2011 15:51, Lodron, Gerald escribió:
<blockquote type="cite">
<div> </div>
<div><span>Hi</span></div>
<div><span></span> </div>
<div><span>Is there a "state-of-the-art" segmentation
filter for robust segmentation of the human body (with lung etc. without
patient table) in a CT data set in ITK? I am pretty sure that I am not the
first one which needs that...</span></div>
<div><span></span> </div>
<div><span>If not, does anyone knows a good algorithm
for that. I made one on my own with region growing but get sometimes problems
with the patient table...</span></div>
<div><span></span> </div>
<div><span>best regards,</span></div>
<div><span>Gerald</span></div><pre><fieldset></fieldset>
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