Automatic alignment of stacks of filament data

Authors:
V. J. Dercksen;B. Weber;D. Günther;M. Oberlaender;S. Prohaska;H.-C. Hege
Affiliations:
Zuse Institute Berlin, Berlin, Germany;Zuse Institute Berlin, Berlin, Germany;Zuse Institute Berlin, Berlin, Germany;Max Planck Institute of Neurobiology, Martinsried, Germany;Zuse Institute Berlin, Berlin, Germany;Zuse Institute Berlin, Berlin, Germany
Venue:
ISBI'09 Proceedings of the Sixth IEEE international conference on Symposium on Biomedical Imaging: From Nano to Macro
Year:
2009

Citing 4
Cited 1

Algorithm 457: finding all cliques of an undirected graph

Communications of the ACM
The Softassign Procrustes Matching Algorithm

IPMI '97 Proceedings of the 15th International Conference on Information Processing in Medical Imaging
A new point matching algorithm for non-rigid registration

Computer Vision and Image Understanding - Special issue on nonrigid image registration
Trajectory fusion for three-dimensional volume reconstruction

Computer Vision and Image Understanding

2011 Special Issue: Simulation of signal flow in 3D reconstructions of an anatomically realistic neural network in rat vibrissal cortex

Neural Networks

Quantified Score

Hi-index	0.00

Visualization

Abstract

We present a fast and robust method for the alignment of image stacks containing filamentous structures. Such stacks are usually obtained by physical sectioning a specimen, followed by an optical sectioning of each slice. For reconstruction, the filaments have to be traced and the sub-volumes aligned. Our algorithm takes traced filaments as input and matches their endpoints to find the optimal transform. We show that our method is able to quickly and accurately align sub-volumes containing neuronal processes, acquired using brightfield microscopy. Our method also makes it possible to align traced microtubuli, obtained from electron tomography data, which are extremely difficult to align manually.