[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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