Combined segmentation and tracking of neural stem-cells
Journal article, 2005

In this paper we analyze neural stem/progenitor cells in an time-lapse image sequence. By using information about the previous positions of the cells, we are able to make a. better selection of possible cells out of a collection of blob-like objects. As a blob detector we use Laplacian of Gaussian (LoG) filters at multiple scales, and the cell contours of the selected cells are segmented using dynamic programming. After the segmentation process the cells are tracked in the sequence using a. combined nearest-neighbor and correlation matching technique. An evaluation of the system show that 95% of the cells were correctly segmented and tracked between consecutive frames.

Author

Karin Althoff

Chalmers, Signals and Systems, Signalbehandling och medicinsk teknik, Imaging and Image Analysis

Johan Degerman

Chalmers, Signals and Systems, Signalbehandling och medicinsk teknik, Imaging and Image Analysis

Tomas Gustavsson

Chalmers, Signals and Systems, Signalbehandling och medicinsk teknik, Imaging and Image Analysis

Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)

03029743 (ISSN) 16113349 (eISSN)

Vol. 3540 282-291

Subject Categories

Computer Vision and Robotics (Autonomous Systems)

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Created

10/6/2017