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

Tue May 31 14:21:41 EDT 2005

Hi Jim,

Well, while embarrassing to admit, I had not been able to find a Markov 
Random Field image filter the last time I looked for it in ITK, so I 
assumed it did not exist. I guess I didn't look hard enough, else I 
would have found the MRFImageFilter class to which you refer, and which 
seems (modulo Simon's points, which appear to be addressable by 
subclassing this filter) to do the job.

One (minor!) point: given the typical stringency of the ITK naming 
style I had assumed that any filter that dealt in Markov Random Fields 
would be named MarkovRandomFieldImageFilter. I suspect this is why I 
overlooked "MRFImageFilter" -- it just got lost in the sea of more 
detailed names. I'm not suggesting re-naming an extant class, but is 
there any good way to get a more descriptively-named MRF image filter 
class in there, while not breaking compatibility with code that uses 
"MRFImageFilter"?

Zach

On May 31, 2005, at 8:56 AM, Miller, James V ((Research)) wrote:

> 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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>>
>
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