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dc.contributor.authorZhou, L.-
dc.contributor.authorWang, L.-
dc.contributor.authorShen, C.-
dc.contributor.authorBarnes, N.-
dc.identifier.citationProceedings of 13th International Conference on Medical Image Computing and Computer Assisted Intervention (MICCAI'10), 20-24th September, 2010 / T. Jiang, N. Navab, J. P.W. Pluim and M. A. Viergever (eds.); Part II pp.266-273-
dc.description.abstractLandmark-based 3D hippocampal shape classification involves high-dimensional descriptor space, many noisy and redundant features, and a very small number of training samples. Feature selection becomes critical in this situation, because it not only improves classification performance, but also identifies the regions that contribute more to shape discrimination. This work identifies the drawbacks of SVM-RFE, and proposes a novel class-separability-based feature selection approach to overcome them. We formulate feature selection as a constrained integer optimization and develop a new algorithm to efficiently and optimally solve this problem. Theoretical analysis and experimental study on both synthetic data and real hippocampus data demonstrate its superior performance over the prevailing SVM-RFE. Our work provides a new efficient feature selection tool for hippocampal shape classification.-
dc.description.statementofresponsibilityLuping Zhou, Lei Wang, Chunhua Shen and Nick Barnes-
dc.relation.ispartofseriesLecture Notes in Computer Science; Vol. 6362-
dc.rights© Springer, Part of Springer Science+Business Media-
dc.subjectAlzheimer Disease-
dc.subjectImage Interpretation, Computer-Assisted-
dc.subjectImaging, Three-Dimensional-
dc.subjectMagnetic Resonance Imaging-
dc.subjectImage Enhancement-
dc.subjectSensitivity and Specificity-
dc.subjectReproducibility of Results-
dc.subjectPattern Recognition, Automated-
dc.titleHippocampal shape classification using redundancy constrained feature selection-
dc.typeConference paper-
dc.contributor.conferenceInternational Conference on Medical Image Computing and Computer Assisted Intervention (MICCAI) (13th : 2011 : Beijing, China)-
dc.publisher.placeNew York-
dc.identifier.orcidShen, C. [0000-0002-8648-8718]-
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Computer Science publications

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