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dc.contributor.authorMalcolm, James G.
dc.contributor.authorRathi, Yogesh
dc.contributor.authorShenton, Martha E.
dc.contributor.authorTannenbaum, Allen R.
dc.date.accessioned2009-07-31T19:49:55Z
dc.date.available2009-07-31T19:49:55Z
dc.date.issued2008-09
dc.identifier.citationJames Malcolm, Yogesh Rathi, Martha E. Shenton, Allen Tannenbaum, "Label Space: A Coupled Multi-Shape Representation," Medical Image Computing and Computer-Assisted Intervention – MICCAI 2008, 11th International Conference, New York, NY, USA, September 6-10, 2008, Proceedings, Part II, Dimitris Metaxas, Leon Axel, Gabor Fichtinger, and Gábor Székely, editors, Lecture Notes in Computer Science, Vol. 5242 (2008) 416-424en
dc.identifier.isbn978-3-540-78274-2
dc.identifier.issn0302-9743
dc.identifier.urihttp://hdl.handle.net/1853/29402
dc.descriptionThe original publication is available at www.springerlink.com: http://dx.doi.org/1 10.1007/978-3-540-85990-1_50
dc.descriptionDOI: 10.1007/978-3-540-85990-1_50
dc.description.abstractRichly labeled images representing several sub-structures of an organ occur quite frequently in medical images. For example, a typical brain image can be labeled into grey matter, white matter or cerebrospinal fluid, each of which may be subdivided further. Many manipulations such as interpolation, transformation, smoothing, or registration need to be performed on these images before they can be used in further analysis. In this work, we present a novel multi-shape representation and compare it with the existing representations to demonstrate certain advantages of using the proposed scheme. Specifically, we propose label space, a representation that is both flexible and well suited for coupled multishape analysis. Under this framework, object labels are mapped to vertices of a regular simplex, e.g. the unit interval for two labels, a triangle for three labels, a tetrahedron for four labels, etc. This forms the basis of a convex linear structure with the property that all labels are equally spaced. We will demonstrate that this representation has several desirable properties: algebraic operations may be performed directly, label uncertainty is expressed equivalently as a weighted mixture of labels or in a probabilistic manner, and interpolation is unbiased toward any label or the background. In order to demonstrate these properties, we compare label space to signed distance maps as well as other implicit representations in tasks such as smoothing, interpolation, registration, and principal component analysis.en
dc.language.isoen_USen
dc.publisherGeorgia Institute of Technologyen
dc.subjectMedical image analysis
dc.subjectMultishape analysis
dc.subjectLabel space
dc.titleLabel Space: A Coupled Multi-shape Representationen
dc.typePost-printen
dc.contributor.corporatenameBrigham and Women's Hospital. Psychiatry Neuroimaging Laboratory
dc.contributor.corporatenameGeorgia Institute of Technology. School of Electrical and Computer Engineering
dc.publisher.originalSpringer Verlag


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