[Insight-users] ITK ROAD MAP 2005-2006 : Call for feedback

Tue May 31 14:26:25 EDT 2005

Hi Zach,Martin and other interested users/developers,
It is great to hear about emerging interest in enhancing support for MRF
filters in ITK. I would like to add a couple of things to Jim's  mail. 

The itk::MRFImageFilter  class implements Besag's classical MRF filter
where the classification labels are iteratively updated . This class was
envisioned to be a base implementation, which could be extended to other
MRF realizations. One idea would be to capture the different classes of
MRFs the ITK users community would like to have. It would be very
helpful to hear from someone in the community who has worked in this
area and is willing to share their experiences.

Thanks,
Sayan

 Date: Tue, 31 May 2005 11:56:07 -0400
From: "Miller, James V (Research)" <millerjv at crd.ge.com>
Subject: RE: [Insight-users] ITK ROAD MAP 2005-2006 : Call for
	feedback
To: "Zachary Pincus" <zpincus at stanford.edu>,	"ITK mailing"
	<insight-users at itk.org>
Message-ID:

<FA26BEF1EA775E4584FB34B91E14A1C4B419A2 at SCHMLVEM01.e2k.ad.ge.com>
Content-Type: text/plain;	charset="Windows-1252"

Zachary, 

Is the current itk::MRFImageFilter not sufficient?  Does it need to be
refactored to accomadate different MRF algorithms?

I only used the MRFImageFilter once. It seemed to perform as I would
have
expected (from reading some of the literature).

One thing I think ITK probably needs are more techniques for learning
the 
pdf's for each class of material.

Jim

-----Original Message-----
From: insight-users-bounces+millerjv=crd.ge.com at itk.org
[mailto:insight-users-bounces+millerjv=crd.ge.com at itk.org]On Behalf Of
Zachary Pincus
Sent: Saturday, May 28, 2005 7:37 PM
To: ITK mailing
Subject: Re: [Insight-users] ITK ROAD MAP 2005-2006 : Call for feedback

Hi all,

After looking over the 2005-2006 ITK roadmap, I've also got a couple of 
questions/comments on the machine learning aspects.

Specifically, to what ends are classification algorithms (e.g. gaussian 
mixture models, k-nearest neighbors, putative neural networks or SVMs) 
present in ITK? It strikes me that one major use of such algorithms in 
medical imaging is for classification of image pixels into various 
tissue types, e.g. grey matter vs. white matter.

If this is the case, I would think that adding Markov Random Field 
capabilities to ITK would be a big win. Basically, MRFs allow users to 
add priors about the *spatial* distribution of various pixel types into 
the classification process. For example, a single isolated pixel 
initially labeled as "grey matter" in a blob of white matter might 
(depending on the priors) be considered an unlikely configuration and 
thus be re-labeled in the final MRF configuration. Such spatial 
considerations are ignored by traditional classifiers.

Because spatial information is so important, and MRFs are a relatively 
easy way to add simple spatial priors, they have become quite popular 
in the image processing literature. I think filters to estimate the MAP 
MRF given an input "label images" (e.g the results of pixel-wise 
classification) would be a very valuable addition, especially if the 
stable of pixel classification methods in ITK is to expand.

Now, I haven't described in too much detail how MRF models actually 
work. A Google Scholar search for "Markov random field image" will show 
the breadth of utilization of MRFs in the imaging literature. Here is a 
good introduction to MRF segmentation, with specific reference to MRI 
images:
Segmentation of brain MR images through a hidden Markov random field 
model and the expectation-maximization algorithm.
Y Zhang, M Brady, S Smith - IEEE Trans Med Imaging, 2001
http://www.cvmt.dk/~hja/teaching/cv/HMRF_EM_BRAIN.pdf

I would be happy to discuss at (much) more length how a MRF 
"classification cleanup" filters could be implemented in ITK, if there 
is any interest in these methods.

Zach

On May 27, 2005, at 2:46 PM, Lino Ramirez wrote:

> Hi Luis and ITK Users/Developers,
>
> I had a brief look at the ITK roadmap 2005-2006. It looks quite
> impressive. I cannot wait until having available all these tools in
one
> single package ;-)
>
> I have some small comments/questions about functionalities I would 
> like to
> see in the toolkit.
>
> I noticed that Neural Networks will be added to the toolkit. Are there

> any
> plans for adding a Support Vector Machines (SVM) [1] implementation? 
> SVM
> have been used successfully in a variety of applications that could be

> of
> interest to the ITK community (see [2] for some sample applications).
> Moreover, it is always good to have a machine learning approach that
is
> similar to the neural networks in architecture but that uses a 
> different
> learning strategy. In this way, one could try the two of them and
> determine which one is more appropriate for a particular dataset.
> Sometimes, in datasets in which the neural networks fail the SVM 
> succeed
> and vice versa.
>
> Are there any plans (even in the very long term) to add support for 
> Fuzzy
> Sets [3], Fuzzy Geometry [4], and Fuzzy Spatial Relations [5] between
> objects in an image. I think these concepts would be invaluable in the
> future of medical image analysis. For example, when we want to measure
> geometric properties in objects in an image, we find that generally
the
> objects are not crisply defined (due to errors during the
segmentation,
> errors in the acquisition of the image, or errors in the definition of

> the
> object -where do the ribs start and the vertebrae end in a spine 
> X-ray).
> In this case, fuzzy geometry could be used to compute the object
> properties. Another example would be in the identification of objects 
> in
> the images. For instance, in the internal brain structures, the right
> caudate nucleus should be closer to the right lateral ventricle than
to
> the left lateral ventricle. Fuzzy spatial relations with the help of 
> fuzzy
> logic [6] could be used to develop a system that makes use of that 
> piece
> of information to identify right lateral ventricle.
>
> Well, those are my two picks ;-)
>
> I am looking forward to any comment
>
> Take care
>
> Lino
>
> [1] C. Cortes and V. Vapnik, "Support-Vector Networks," Machine 
> Learning,
> vol. 20, pp. 273-297, 1995
> [2] http://www.clopinet.com/isabelle/Projects/SVM/applist.html
> [3] L.A. Zadeh, "Fuzzy sets," Information and Control, vol. 8, pp. 
> 38-352,
> 1965
> [4] A. Rosenfeld, "Fuzzy geometry: An updated overview," Information
> Sciences, vol. 110, pp. 127-133, 1998
> [5] I. Bloch, "Fuzzy spatial relationships for image processing and
> interpretation: a review," Image and Vision Computing, vol. 23, pp.
> 89-110, 2005
> [6] L.A. Zadeh, "Outline of a new approach to the analysis of complex
> systems and decision processes," IEEE Transactions on Systems, Man,
and
> Cybernetics, vol. SMC-3, no. 1, pp. 28-44, 1973
>
>> A first draft of the road map for ITK development/maintenance has
>> been crafted for the period of September 2005 - September 2006.
>>
>>
>> You will find this draft as a link to the Oversight Committee page
>>
>> http://www.itk.org/Wiki/ITK_Oversight_Committee
>>
>>
>> More specifically at
>>
>>
>> http://www.itk.org/Wiki/ITK_Roadmap_2005_2006
>>
>>
>> The purpose of this road map is to plan for features and 
>> functionalities
>> to be included in ITK in the near/medium term (1 to 2 years).
>>
>> The addition of these features should make of ITK a better tool for
>> supporting your efforts in medical research, and development of 
>> medical
>> applications.
>>
>> The road map also includes the maintenance tasks to be undertaken in
>> ITK. This may involve refactoring of classes, deprecation of classes,
>> additional testing, additional coverage, improvements on tutorials
and
>> so on.
>>
>>
>> Please let us know of the features that you would like to see in ITK
>> in the upcoming future, and what points of the toolkit you consider
>> that can be improved in order to better server the community.
>>
>>
>>
>> Thanks
>>
>>
>>
>> Luis

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