[Insight-users] Circle detection in itk

[Gaëtan Lehmann]

Hi Ali,
Hi Luis,

I guess this mail reply to some of the question I wanted to ask after your  
last mail about particle detection - great :-)

Le Sat, 06 Jan 2007 14:10:53 +0100, Ali - <saveez at hotmail.com> a écrit:

> Hi Luis,
> Thanks for the reply, I thought the itk community was dead in the new  
> year.

some of us may have survived to their holidays ;-)

> I need to detect the position and diameter of randomly distributed  
> particle-images where it is assumed each particle-image has a Gaussian  
> profile.  > 1) How fast do you need the circle detection to be ?
> The circle detection algorithm must be fast, it is applied to an  
> ensemble of particle-images with Gaussian profile. Each image may  
> contain tens to hundreds of particle-images.

Are there only circluar particles in your image or also other shapes ?
In the case there are only circular particules, a simple thresholding and  
a labelisation of the connected components may be enough. If the particles  
are on a non uniform background, you can first apply a top-hat to remove  
it. If the intensity of the particules is not uniform from image to image,  
it may be difficult to find a good threshold value, but some automatic  
thresholding methods may give good results.
If there are other shapes, what are they ? How big are they compared to  
the particules ?
It would help if you can give a link to an example.

Gaetan



>> 2) How big are your images ?
> About 1k X 1k. Each particle-image could have a diameter from 3 to 10  
> pixels.
>> 3) Are you looking for full circles ?> or only for circumferences ?
> The circle (particle-image) centre and diameter (Gaussian profile  
> diameter) are desirable to be detected.
>> > 4) Do you expect to find multiple circles per image ?
> As described, yes.
>> > 5) Do you have any priori information about the circle ?> (eg.  
>> expected positions, expected radius, expected> intensities)
> Positions are random, diameters are variable (3 to 10 pixels) the  
> intensities are variable too. The only prior knowledge could be the  
> assumed Gaussian profile of each particle-image. Correlation-based  
> methods may work, but I am sure there are faster methods around. The use  
> of the existing code in the library is preferred.
>>> 6) Have you profiled the time that it takes to run the> Hough circle  
>>> detection in a release (optimized) build ?>
> No, but the following is a quote from 'Practical Algorithms for Image  
> Processing', Seul et al, Cambridge University Press 2000, 4.10.2:
> '..., and it is for this reason that the Hough transform is far less  
> popular for geometrical objects than the line'.
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-- 
Gaëtan Lehmann
Biologie du Développement et de la Reproduction
INRA de Jouy-en-Josas (France)
tel: +33 1 34 65 29 66    fax: 01 34 65 29 09
http://voxel.jouy.inra.fr