[Insight-users] ITK ROAD MAP 2005-2006 : Call for feedback
Gee, James
GeeJames at uphs.upenn.edu
Tue May 31 15:12:22 EDT 2005
dear david:
we have an A2D2 contract to implement graph cuts, and hope to have something
to show soon.
jim
> -----Original Message-----
> From: insight-users-bounces+gee=rad.upenn.edu at itk.org
> [mailto:insight-users-bounces+gee=rad.upenn.edu at itk.org]
> Sent: Tuesday, May 31, 2005 2:59 PM
> Cc: insight-users at itk.org
> Subject: RE: [Insight-users] ITK ROAD MAP 2005-2006 : Call
> for feedback
>
> 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
> >
> >
> >
> _______________________________________________
> Insight-users mailing list
> Insight-users at itk.org
> http://www.itk.org/mailman/listinfo/insight-users
>
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