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

Tue May 31 14:59:12 EDT 2005

hi gang,

one additional comment--if MRF support in ITK is to be enhanced, i'd 
strongly recommend adding a class to implement the graph cuts approach to 
MRF optimization, which published articles, and my own experience, have 
shown to be consistently better than the old greedy updating methods.

-dh
-david haynor (haynor at u.washington.edu)
department of radiology
box 356004
university of washington
seattle, WA  98195
(206) 543-3320

On Tue, 31 May 2005, Sayan Pathak wrote:

> 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
>
>
>