No subject

Wed Oct 7 22:37:18 EDT 2009

trying to achieve (I don't know what a tomogram is, or carbon
support), but if I've guessed correctly, the attached image may be
fairly close to your desired output. It was generated as follows:
    1. binary threshold (lower =3D 67, upper =3D 255)
    2. morphological close (structuring element =3D circle, radius =3D 30)
    3. voting binary iterative hole filling (radius =3D 12, majority
threshold =3D 25)

Of course you can tweak the cleaning operations as required. Some of
the new label filters in the Review folder may also be useful for
cleaning the segmentation.

Anyway, just an idea.

Cheers, Dan

2009/10/28 Kishore Mosaliganti <kishoreraom at gmail.com>:
> Hi Roman,
>
> From what I see, there is a fair amount of texture in the carbon
> 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 th=
e
>> foreground) for further analysis.
>>
>> Is there any way to get a better representation than the one from the gl=
obal
>> 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 filter=
s
>>>> 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 givin=
g
>>>> a clue of what part I want. It's the carbon support for the small whit=
e
>>>> 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
>>>>
>>>> Helmholtz-Zentrum Berlin f=FCr Materialien und Energie GmbH
>>>> Bereich Funktionale Materialien
>>>> Institut f=FCr angewandte Materialforschung
>>>> Hahn-Meitner-Platz 1
>>>> D-14109 Berlin
>>>>
>>>> Tel.: +49-(0)30-8062-2816
>>>> Fax.: +49-(0)30-8062-3059
>>>>
>>>> Mitglied der Hermann von Helmholtz-Gemeinschaft Deutscher
>>>> Forschungszentren e.V.
>>>> Vorsitzende des Aufsichtsrates: Dr. Beatrix Vierkorn-Rudolph,
>>>> Stellvertretende Vorsitzende: Dr. Jutta Koch-Unterseher,
>>>> Gesch=E4ftsf=FChrer: Prof. Dr. Anke Kaysser-Pyzalla, Prof. Dr. h.c. Wo=
lfgang
>>>> Eberhardt, Dr. Ulrich Breuer =A0Sitz Berlin, AG Charlottenburg, 89 HRB=
 5583
>>>>
>>>>
>>>>
>>>>
>>>>
>>>> _____________________________________
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>>>>
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