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

Sun May 29 08:26:04 EDT 2005

Hi Zach,

you are perfectly right and I vote with both hands for having MRF in ITK.
I have lots of great experiences with segmentation implementation (in FSL,
fMRIB Oxford) with MRF described by Zhang et al.

I hope to get a master-student to get this work done next year, if MRF
would not make to the ITK's road map.

Cheers,

Martin

On Sat, 28 May 2005, Zachary Pincus wrote:

> 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
> >>
> >
> > _______________________________________________
> > Insight-users mailing list
> > Insight-users at itk.org
> > http://www.itk.org/mailman/listinfo/insight-users
> >
>
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