liu.seSearch for publications in DiVA
Endre søk
RefereraExporteraLink to record
Permanent link

Direct link
Referera
Referensformat
  • apa
  • harvard1
  • ieee
  • modern-language-association-8th-edition
  • vancouver
  • oxford
  • Annet format
Fler format
Språk
  • de-DE
  • en-GB
  • en-US
  • fi-FI
  • nn-NO
  • nn-NB
  • sv-SE
  • Annet språk
Fler språk
Utmatningsformat
  • html
  • text
  • asciidoc
  • rtf
Manifold learning and representations for image analysis and visualization
Linköpings universitet, Institutionen för medicinsk teknik, Medicinsk informatiK. Linköpings universitet, Tekniska högskolan.
2006 (engelsk)Licentiatavhandling, med artikler (Annet vitenskapelig)
Abstract [en]

We present a novel method for manifold learning, i.e. identification of the low-dimensional manifold-like structure present in a set of data points in a possibly high-dimensional space. The main idea is derived from the concept of Riemannian normal coordinates. This coordinate system is in a way a generalization of Cartesian coordinates in Euclidean space. We translate this idea to a cloud of data points in order to perform dimension reduction. Our implementation currently uses Dijkstra's algorithm for shortest paths in graphs and some basic concepts from differential geometry. We expect this approach to open up new possibilities for analysis of e.g. shape in medical imaging and signal processing of manifold-valued signals, where the coordinate system is “learned” from experimental high-dimensional data rather than defined analytically using e.g. models based on Lie-groups.

We propose a novel post processing method for visualization of fiber traces from DT-MRI data. Using a recently proposed non-linear dimensionality reduction technique, Laplacian eigenmaps (Belkin and Niyogi, 2002), we create a mapping from a set of fiber traces to a low dimensional Euclidean space. Laplacian eigenmaps constructs this mapping so that similar traces are mapped to similar points, given a custom made pairwise similarity measure for fiber traces. We demonstrate that when the low-dimensional space is the RGB color space, this can be used to visualize fiber traces in a way which enhances the perception of fiber bundles and connectivity in the human brain.

sted, utgiver, år, opplag, sider
Institutionen för medicinsk teknik , 2006. , s. 116
Serie
Linköping Studies in Science and Technology. Thesis, ISSN 0280-7971 ; 1235
Emneord [en]
manifold learning, image analysis, signal processing, diffusion tensor mri
HSV kategori
Identifikatorer
URN: urn:nbn:se:liu:diva-7559ISBN: 91-85497-33-9 (tryckt)OAI: oai:DiVA.org:liu-7559DiVA, id: diva2:22566
Presentation
2006-03-10, Seminarierummet plan 13, Institutionen för medicinsk teknik, 581 85 LINKÖPING, LINKÖPING, 10:00 (engelsk)
Opponent
Veileder
Merknad
Report code: LIU-TEK-LIC-2006:16Tilgjengelig fra: 2006-10-16 Laget: 2006-10-16 Sist oppdatert: 2013-08-28

Open Access i DiVA

Fulltekst mangler i DiVA

Personposter BETA

Brun, Anders

Søk i DiVA

Av forfatter/redaktør
Brun, Anders
Av organisasjonen

Søk utenfor DiVA

GoogleGoogle Scholar

isbn
urn-nbn

Altmetric

isbn
urn-nbn
Totalt: 745 treff
RefereraExporteraLink to record
Permanent link

Direct link
Referera
Referensformat
  • apa
  • harvard1
  • ieee
  • modern-language-association-8th-edition
  • vancouver
  • oxford
  • Annet format
Fler format
Språk
  • de-DE
  • en-GB
  • en-US
  • fi-FI
  • nn-NO
  • nn-NB
  • sv-SE
  • Annet språk
Fler språk
Utmatningsformat
  • html
  • text
  • asciidoc
  • rtf