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hal.structure.identifierCEntre de REcherches en MAthématiques de la DEcision [CEREMADE]
dc.contributor.authorFiot, Jean-Baptiste*
hal.structure.identifierCEntre de REcherches en MAthématiques de la DEcision [CEREMADE]
dc.contributor.authorCohen, Laurent D.
HAL ID: 738939
*
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dc.contributor.authorBourgeat, Pierrick*
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dc.contributor.authorRaniga, Parnesh*
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dc.contributor.authorAcosta, Oscar*
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dc.contributor.authorVillemagne, Victor*
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dc.contributor.authorSalvado, Olivier*
hal.structure.identifierCSIRO Information and Commuciation Technologies [CSIRO ICT Centre]
dc.contributor.authorFripp, Jürgen*
dc.date.accessioned2012-02-03T15:10:21Z
dc.date.available2012-02-03T15:10:21Z
dc.date.issued2012
dc.identifier.urihttps://basepub.dauphine.fr/handle/123456789/8027
dc.language.isoenen
dc.subjectBrain Imagingen
dc.subjectManifold Learningen
dc.subjectNon Linear Dimensionality Reductionen
dc.subjectPopulation Analysisen
dc.subject.ddc621.3en
dc.titleMultimodality Imaging Population Analysis using Manifold Learningen
dc.typeCommunication / Conférence
dc.contributor.editoruniversityotherThe Mental Health Research Institute University of Melbourne;Australie
dc.contributor.editoruniversityotherLaboratoire Traitement du Signal et de l'Image (LTSI) http://www.ltsi.univ-rennes1.fr/ INSERM : U642 – Université de Rennes 1;France
dc.contributor.editoruniversityotherCSIRO Information and Commuciation Technologies (CSIRO ICT Centre) http://www3.ict.csiro.au/ CSIRO;Australie
dc.description.abstractenCharacterizing the variations in anatomy and tissue properties in large populations is a challenging problem in medical imaging. Various statistical analysis, dimension reduction and clustering techniques have been developed to reach this goal. These techniques can provide insight into the effects of demographic and genetic factors on disease progression. They can also be used to improve the accuracy and remove biases in various image segmentation and registration algorithms. In this paper we explore the potential of some non linear dimensionality reduction (NLDR) techniques to establish simple imaging indicators of ageing and Alzheimers Disease (AD) on a large population of multimodality brain images (Magnetic Resonance Imaging (MRI) and PiB Positron Emission Tomography (PET)) composed of 218 patients including healthy control, mild cognitive impairment and AD. Using T1-weighted MR images, we found using laplacian eigenmaps that the main variation across this population was the size of the ventricles. For the grey matter signal in PiB PET images, we built manifolds that showed transition from low to high PiB retention. The combination of the two modalities generated a manifold with different areas that corresponded to different ventricle sizes and beta-amyloid loads.en
dc.relation.ispartoftitleComputational Vision and Medical Image Processing: VipIMAGE 2011en
dc.relation.ispartofeditorJorge, R.M. Natal
dc.relation.ispartofeditorTavares, João Manuel R.S.
dc.relation.ispartofpublnameCRC Pressen
dc.relation.ispartofpublcityLeidenen
dc.relation.ispartofdate2012
dc.relation.ispartofpages440en
dc.identifier.urlsitehttp://hal.archives-ouvertes.fr/hal-00662345en
dc.description.sponsorshipprivateouien
dc.subject.ddclabelTraitement du signalen
dc.relation.ispartofisbn978-0-415-68395-1en
dc.relation.conftitleVipIMAGE 2011en
dc.relation.confdate2011-10
dc.relation.confcityOlhãoen
dc.relation.confcountryPortugalen
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