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July 2007

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"Classification, clustering, and phylogeny estimation" <[log in to unmask]>
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"Audette, Michel" <[log in to unmask]>
Date:
Fri, 27 Jul 2007 07:56:45 +0200
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Hi Bill, 
 
thanks for your kind reply. I will do a search with your keywords. 
 
I cannot commit to a conference yet, but if you have a link, I'll look into it. Classification is only part of what I do. My goal is patient-specific surgery simulation, and that entails computing a tissue map from CT and occasionally co-registered MR, as well as meshing tissues to synthesize a biomechanical response. Whether I go also depends where I am at the time: for a host of reasons it's trickier if I am still in Leipzig then. 
 
Thanks again for your help. 
 
Cheers,
 
Michel

________________________________

From: Classification, clustering, and phylogeny estimation on behalf of William Shannon
Sent: Thu 7/26/2007 10:12 PM
To: [log in to unmask]
Subject: Re: using features which may be null for some classes


Two thoughts (based on my 2 minutes of understanding of this problem from reading your email):

1. It seems that the 0 values are important and indicate soft tissue.  I am not sure why you couldn't use these discriminators.  If they are 0 in all samples then they won't impact the classifier, if 0 in some then maybe you can classify by these values.

2. It sounds like the data are images and I wonder if work on fnctional data analysis would be useful.  Search the internet on RAMSAY STATISTICS MONTREAL  and you will get to his web page and a link to the book on functional data analysis.


DO YOU WANT TO COME TO ST LOUIS NEXT JULY 5-7 FOR THE 2008 ANNUAL MEETING OF THE CLASSIFICATION SOCIETY AND TALK ABOUT YOUR WORK????


Bill

"Audette, Michel" <[log in to unmask]> wrote: 

	Hi Bill, 
	 
	thanks for your kind reply. One feature is CT data of the head, while the second is a sheetness measure, based on a paper by Descôteaux
	Bone enhancement filtering: application to sinus bone segmentation and simulation of pituitary surgery
	 www-sop.inria.fr/odyssee/team/Maxime.Descoteaux/docs/descoteaux_miccai05.pdf  
	It detects locally curviplanar tissues at various scales, and in fact the scale coinciding with the maximum sheetness value is also stored. 
	 
	This sheetness data is a volume image, and because of the way it is output, using a minc volume where reals are stored as shorts with a scaling factor, any value less than 0.001 is simply output as null in these volumes. So much of soft tissue in the head, except skin which is in fact somewhat sheetlike, as well as background, has a null value, where cranial bones have a high value. 
	 
	If I use a minimally supervised classification algorithm, I have to do it in a manner where one of the features has zero values for some tissues, and non-zero values for others, unless I replace those zero values by a synthetic value for mean and variance, in keeping with that .001 threshold. 
	 
	What do you think?
	 
	Cheers, 
	 
	Michel
	 
	Michel Audette, Ph.D.  
	Innovation Center Computer Assisted Surgery (ICCAS)  
	Philipp-Rosenthal-Strasse 55 
	Leipzig, Germany 
	Phone: ++49 (0) 341 / 97 - 1 20 13 
	Fax: ++49 (0) 341 / 97 - 1 20 09 
	 
	 
	
________________________________

	From: Classification, clustering, and phylogeny estimation [mailto:[log in to unmask]] On Behalf Of William Shannon
	Sent: July 25, 2007 6:59 PM
	To: [log in to unmask]
	Subject: Re: using features which may be null for some classes
	 
	To you explain the data a little more?
	
	Bill Shannon
	
	
	"Audette, Michel" <[log in to unmask]> wrote:
	Dear all, 
	
	I am interested in implemented a semi-supervised clustering method, i.e.: making use of a small set of training points, to classify tissues of the head visible in CT data. I would like not only to use CT intensity as a feature, but a measure of sheet-like structure that correlates with thin bone, and may assist the detection of thin bone structures that are otherwise undiscernible from soft tissue, due to partial volume effects that blurr intensities together. However, this latter feature, sheetness, produces a null value for most tissue classes. 
	
	Can anyone suggest a means of integrating two features together, CT and sheetness, in a clustering algorithm, given that one of them appears null for several classes? 
	
	Best regards, 
	
	Michel Audette, Ph.D.
	Innovation Center Computer Assisted Surgery (ICCAS)
	Philipp-Rosenthal-Strasse 55
	Leipzig, Germany
	Phone: ++49 (0) 341 / 97 - 1 20 13
	Fax: ++49 (0) 341 / 97 - 1 20 09
	
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