Show simple item record

dc.contributor.authorMohan, Vandana
dc.contributor.authorSundaramoorthi, Ganesh
dc.contributor.authorKubicki, Marek
dc.contributor.authorTerry, Douglas
dc.contributor.authorTannenbaum, Allen R.
dc.date.accessioned2010-04-26T19:07:01Z
dc.date.available2010-04-26T19:07:01Z
dc.date.issued2010-02-16
dc.identifier.citationVandana Mohan, Ganesh Sundaramoorthi, Marek Kubicki, Douglas Terry and Allen Tannenbaum, "Population Analysis of the Cingulum Bundle using the Tubular Surface Model for Schizophrenia Detection," Medical Imaging 2010: Computer-Aided Diagnosis, Nico Karssemeijer, Ronald M. Summers, editors, Proc. SPIE, Vol. 7624, 762429 (2010)en_US
dc.identifier.issn0277-786X
dc.identifier.urihttp://hdl.handle.net/1853/32757
dc.description©2010 SPIE--The International Society for Optical Engineering. One print or electronic copy may be made for personal use only. Systematic reproduction and distribution, duplication of any material in this paper for a fee or for commercial purposes, or modification of the content of the paper are prohibited. The electronic version of this article is the complete one and can be found online at: http://dx.doi.org/10.1117/12.844297en_US
dc.descriptionDOI: 10.1117/12.844297
dc.descriptionPresented at Medical Imaging 2010: Computer-Aided Diagnosis, 16 February 2010, San Diego, California, USA
dc.description.abstractWe propose a novel framework for population analysis of DW-MRI data using the Tubular Surface Model. We focus on the Cingulum Bundle (CB) - a major tract for the Limbic System and the main connection of the Cingulate Gyrus, which has been associated with several aspects of Schizophrenia symptomatology. The Tubular Surface Model represents a tubular surface as a center-line with an associated radius function. It provides a natural way to sample statistics along the length of the fiber bundle and reduces the registration of fiber bundle surfaces to that of 4D curves. We apply our framework to a population of 20 subjects (10 normal, 10 schizophrenic) and obtain excellent results with neural network based classification (90% sensitivity, 95% specificity) as well as unsupervised clustering (k-means). Further, we apply statistical analysis to the feature data and characterize the discrimination ability of local regions of the CB, as a step towards localizing CB regions most relevant to Schizophrenia.en_US
dc.language.isoen_USen_US
dc.publisherGeorgia Institute of Technologyen_US
dc.subjectTubular surface modelen_US
dc.subjectDW-MRIen_US
dc.subjectSchizophreniaen_US
dc.subjectPopulation analysisen_US
dc.subjectCingulum bundleen_US
dc.titlePopulation Analysis of the Cingulum Bundle using the Tubular Surface Model for Schizophrenia Detectionen_US
dc.typeProceedingsen_US
dc.contributor.corporatenameGeorgia Institute of Technology. School of Electrical and Computer Engineering
dc.contributor.corporatenameUniversity of California, Los Angeles. Computer Science Dept.
dc.contributor.corporatenameBrigham and Women’s Hospital. Dept. of Radiology. Surgical Planning Laboratory
dc.publisher.originalInternational Society for Optical Engineering


Files in this item

Thumbnail

This item appears in the following Collection(s)

Show simple item record