direkt zum Inhalt springen

direkt zum Hauptnavigationsmenü

Sie sind hier

TU Berlin

Page Content

Analysis of Neural Data

All Publications

Warping with Optimized Weighting Factors of Displacement Vectors - A New Method to Reduce Inter-Individual Variations in Brain Imaging
Citation key Pielot2000c
Author Pielot, R. and Scholz, M. and Obermayer, K. and Gundelfinger, E. and Hess, A.
Title of Book 4th IEEE Southwest Symposium on Image Analysis and Interpretation
Pages 264 – 268
Year 2000
ISBN 0-7695-0595-3
DOI 10.1109/IAI.2000.839612
Editor Horsch A. and Lehmann T.
Publisher IEEE
Abstract An accurate comparison of multimodal and/or inter-individual 3D image datasets of brains requires geometric transformation techniques (warping) to reduce geometric variations. Here, a subset of warping techniques, namely point-based warping, is investigated. For this kind of warping landmarks between datasets have to be defined. In large 3D datasets manually setting of landmarks is time-consuming and therefore impracticable. Consequently we approach this problem by investigating fast automatic procedures for determining landmarks, based on Monte-Carlo-techniques. The combined methods were tested on 3D autoradiographs of brains of gerbils. The results are evaluated by three different similarity functions. We found that the combined approach is highly applicable in processing brain images.
Link to publication Download Bibtex entry

Zusatzinformationen / Extras

Quick Access:

Schnellnavigation zur Seite über Nummerneingabe

Auxiliary Functions