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Multimodality Imaging Population Analysis using Manifold Learning

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Date
2012
Link to item file
http://hal.archives-ouvertes.fr/hal-00662345
Dewey
Traitement du signal
Sujet
Brain Imaging; Manifold Learning; Non Linear Dimensionality Reduction; Population Analysis
Conference name
VipIMAGE 2011
Conference date
10-2011
Conference city
Olhão
Conference country
Portugal
Book title
Computational Vision and Medical Image Processing: VipIMAGE 2011
Author
Jorge, R.M. Natal; Tavares, João Manuel R.S.
Publisher
CRC Press
Publisher city
Leiden
Year
2012
Pages number
440
ISBN
978-0-415-68395-1
URI
https://basepub.dauphine.fr/handle/123456789/8027
Collections
  • CEREMADE : Publications
Metadata
Show full item record
Author
Fiot, Jean-Baptiste
60 CEntre de REcherches en MAthématiques de la DEcision [CEREMADE]
Cohen, Laurent D.
60 CEntre de REcherches en MAthématiques de la DEcision [CEREMADE]
Bourgeat, Pierrick
Raniga, Parnesh
Acosta, Oscar
Villemagne, Victor
Salvado, Olivier
Fripp, Jürgen
6181 CSIRO Information and Commuciation Technologies [CSIRO ICT Centre]
Type
Communication / Conférence
Abstract (EN)
Characterizing 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.

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