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support. The background is flat.

Perhaps, if you were to run a gradient magnitude filter with an large
sigma, and then apply a threshold on gradient magnitude, you will be
able to see the foreground.

However, there is a caveat. This will not give you precise boundaries
of the carbon support.

Kishore

On Wed, Oct 28, 2009 at 11:08 AM, Roman Grothausmann
<roman.grothausmann at helmholtz-berlin.de> wrote:
> Hello Kishore,
>
> Kishore Mosaliganti wrote:
>>
>> Hi,
>>
>> Your link needs a password.
>
> user: pubu; pass: pp09
>
>> Are you interested in separating the foreground from the background or
>> delineating each componet from the other.
>
> I would like to get a binary representation of the carbon support (as the
> foreground) for further analysis.
>
> Is there any way to get a better representation than the one from the glo=
bal
> threshold?
>
> Thanks for looking into this.
> Roman
>
>> On Wed, Oct 28, 2009 at 7:30 AM, Roman Grothausmann
>> <roman.grothausmann at helmholtz-berlin.de> wrote:
>>>
>>> Dear mailing list members,
>>>
>>>
>>> I've read about FC, MRF and all those fancy hybrid segmentation filters
>>> described in "Insight into Images" but still I've hard times seeing
>>> which segmentation filter would be best for my tomogram problem.
>>> Here's a slice (user: pubu; pas: pp09):
>>> http://www.helmholtz-berlin.de/files/roman.grothausmann/pub/
>>> zs_1350x950x660_xz_272_03.png shows an overall threshold that is giving
>>> a clue of what part I want. It's the carbon support for the small white
>>> particles. I think for humans its much easier to see
>>> (zs_1350x950x660_xz_272_01.png) because we can recognise the pattern
>>> that makes up the carbon support. Is there some segmentation technique
>>> based on pattern recognition available in ITK? Granulometry sounds
>>> promising but I couldn't find a filter doing that.
>>>
>>> Any help is very much appreciated
>>> Roman
>>>
>>>
>>> --
>>> Roman Grothausmann
>>>
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