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:[EMAIL PROTECTED] On Behalf Of William Shannon Sent: July 25, 2007 6:59 PM To: [email protected] Subject: Re: using features which may be null for some classes To you explain the data a little more? Bill Shannon "Audette, Michel" <[EMAIL PROTECTED]> 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 ---------------------------------------------- CLASS-L list. Instructions: http://www.classification-society.org/csna/lists.html#class-l ---------------------------------------------- CLASS-L list. Instructions: http://www.classification-society.org/csna/lists.html#class-l ---------------------------------------------- CLASS-L list. Instructions: http://www.classification-society.org/csna/lists.html#class-l
