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Conference Paper: Probabilistic segmentation of volume data for visualization using SOM-PNN classifier

TitleProbabilistic segmentation of volume data for visualization using SOM-PNN classifier
Authors
KeywordsComputers
Computer graphics
Issue Date1998
PublisherIEEE.
Citation
IEEE Symposium on Volume Visualization, Research Triangle Park, NC., 19-20 October 1998. In Conference Proceedings, 1998, p. 71-78 How to Cite?
AbstractWe present a new probabilistic classifier, called SOM-PNN classifier, for volume data classification and visualization. The new classifier produces probabilistic classification with Bayesian confidence measure which is highly desirable in volume rendering. Based on the SOM map trained with a large training data set, our SOM-PNN classifier performs the probabilistic classification using the PNN algorithm. This combined use of SOM and PNN overcomes the shortcomings of the parametric methods, the nonparametric methods, and the SOM method. The proposed SOM-PNN classifier has been used to segment the CT sloth data and the 20 human MRI brain volumes resulting in much more informative 3D rendering with more details and less artifacts than other methods. Numerical comparisons demonstrate that the SOM-PNN classifier is a fast, accurate and probabilistic classifier for volume rendering.
Persistent Identifierhttp://hdl.handle.net/10722/45604

 

DC FieldValueLanguage
dc.contributor.authorMa, Fen_HK
dc.contributor.authorWang, WPen_HK
dc.contributor.authorTsang, WWen_HK
dc.contributor.authorTang, Zen_HK
dc.contributor.authorXia, Sen_HK
dc.contributor.authorTong, Xen_HK
dc.date.accessioned2007-10-30T06:30:06Z-
dc.date.available2007-10-30T06:30:06Z-
dc.date.issued1998en_HK
dc.identifier.citationIEEE Symposium on Volume Visualization, Research Triangle Park, NC., 19-20 October 1998. In Conference Proceedings, 1998, p. 71-78en_HK
dc.identifier.urihttp://hdl.handle.net/10722/45604-
dc.description.abstractWe present a new probabilistic classifier, called SOM-PNN classifier, for volume data classification and visualization. The new classifier produces probabilistic classification with Bayesian confidence measure which is highly desirable in volume rendering. Based on the SOM map trained with a large training data set, our SOM-PNN classifier performs the probabilistic classification using the PNN algorithm. This combined use of SOM and PNN overcomes the shortcomings of the parametric methods, the nonparametric methods, and the SOM method. The proposed SOM-PNN classifier has been used to segment the CT sloth data and the 20 human MRI brain volumes resulting in much more informative 3D rendering with more details and less artifacts than other methods. Numerical comparisons demonstrate that the SOM-PNN classifier is a fast, accurate and probabilistic classifier for volume rendering.en_HK
dc.format.extent745607 bytes-
dc.format.extent3046 bytes-
dc.format.extent3373 bytes-
dc.format.mimetypeapplication/pdf-
dc.format.mimetypetext/plain-
dc.format.mimetypetext/plain-
dc.languageengen_HK
dc.publisherIEEE.en_HK
dc.relation.ispartofIEEE Symposium on Volume Visualization-
dc.rightsCreative Commons: Attribution 3.0 Hong Kong License-
dc.rights©1998 IEEE. Personal use of this material is permitted. However, permission to reprint/republish this material for advertising or promotional purposes or for creating new collective works for resale or redistribution to servers or lists, or to reuse any copyrighted component of this work in other works must be obtained from the IEEE.en_HK
dc.subjectComputersen_HK
dc.subjectComputer graphicsen_HK
dc.titleProbabilistic segmentation of volume data for visualization using SOM-PNN classifieren_HK
dc.typeConference_Paperen_HK
dc.description.naturepublished_or_final_versionen_HK
dc.identifier.doi10.1109/SVV.1998.729587en_HK
dc.identifier.hkuros40696-

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