Hi,<br><br>If your goal is to compute the gradient image, the standard thing to do is to first smooth with a Gaussian (a real Gaussian, not diffusion). This is because the derivative operator itself outputs the slope of the curve between neighboring pixels, so if you've got any kind of noise in the image, it will magnify it in the output. When you smooth the image, it suppresses the noise.<br>
<br>Anyway, that's the text-book approach. Anisotropic smoothing might work better in some applications, but I don't know if it's ever been used to compute the gradient.<br><br>BTW, note that GradientRecursiveGaussianImageFilter will smooth the image and then take the gradient for you in one step, so that can be convenient.<br>
<br>- Aviv<br><br><br><br><div class="gmail_quote">On Fri, Jun 6, 2008 at 1:10 PM, Xavier Mellado Esteban <<a href="mailto:xme@unizar.es">xme@unizar.es</a>> wrote:<br><blockquote class="gmail_quote" style="border-left: 1px solid rgb(204, 204, 204); margin: 0pt 0pt 0pt 0.8ex; padding-left: 1ex;">
<br>
Hello,<br>
<br>
You should take a look at ITK User's Guide, if you haven't done it yet:<br>
<br>
<a href="http://www.itk.org/ItkSoftwareGuide.pdf" target="_blank">http://www.itk.org/ItkSoftwareGuide.pdf</a><br>
<br>
"Section 6.7 Smoothing Filters" discusses a bit the gaussian and the anisotric filters.<br>
<br>
Extracted from the user's guide:<br>
<br>
<blockquote class="gmail_quote" style="border-left: 1px solid rgb(204, 204, 204); margin: 0pt 0pt 0pt 0.8ex; padding-left: 1ex;">
The drawback of image denoising (smoothing) is that it tends to blur away the sharp boundaries<br>
in the image that help to distinguish between the larger-scale anatomical structures that one<br>
is trying to characterize (which also limits the size of the smoothing kernels in most applications).<br>
Even in cases where smoothing does not obliterate boundaries, it tends to distort the fine<br>
structure of the image and thereby changes subtle aspects of the anatomical shapes in question.<br>
</blockquote>
<br>
<br>
I have used them a bit and the main difference is what they say. Theorically, the gaussian filter will help to denoise and to have smooth derivatives, but it will blur the borders of "small" structures on your image. On the contrary, the anisotropic will tend to preserve the borders, but I do not know if it has any bad influence on the derivatives.<br>
<br>
In any case, the use of one filter or the other will depend a lot on the kind of image and which are the structures you want to study. Besides, the parameters used in each filter will change substantially the results, so you have to tune them for your particular case.<br>
<br>
My understanding is, if your image is not really noisy and you want to help the derivatives, you could smooth it a bit with the gaussian (check first that your gradient filter do not smooth it again). If your image is noisy, try to denoise it with the anisotropic, and then apply the gaussian as I told before. My advice is, try to avoid the gaussian as a denoise filter unless your structures are really big.<br>
<br>
Wait for other users, probably they can give you better answers. I hope this helps you.<br>
<br>
Regards,<br>
Xavi<br>
<br>
<br>
<br>
<br>
Leila Baghdadi <<a href="mailto:baghdadi@phenogenomics.ca" target="_blank">baghdadi@phenogenomics.ca</a>> ha escrito:<br>
<br>
<blockquote class="gmail_quote" style="border-left: 1px solid rgb(204, 204, 204); margin: 0pt 0pt 0pt 0.8ex; padding-left: 1ex;">
Hi guys<br>
<br>
does anyone know the difference between the above two and the pros and<br>
cons of using each for before creating the image gradient for<br>
segmentation<br>
<br>
<br>
thanks<br>
<br>
Leila<br>
<br>
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<br>
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