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Automatic tracking of neural stem cells in sequential digital images

Paper ID Volume ID Publish Year Pages File Format Full-Text
5110 341 2016 10 PDF Available
Title
Automatic tracking of neural stem cells in sequential digital images
Abstract

Neural stem cells are the cells that give rise to the main cell types of the nervous system. Due to their varying size and shape, and random movement, the tracking of these cells in suspension in video sequences is challenging. This paper develops an automatic tracking system for neural stem cells. The system first detects and localizes cells in the image sequence, followed by a feature extraction step for the subsequent cell tracking. Then, the system tracks inactive cells using an improved mean shift algorithm, divisive cells through a context-based technique, and active cells by means of dynamic local prediction (DLP) and gray prediction (GP) algorithms. Experimental results show that the proposed system not only improves the accuracy of fast moving tracking, but also constructs accurately the trajectories of the cell movement and reduces the iterations during the center searching.

Keywords
Cell tracking; Improved mean shift; Dynamic local prediction (DLP); Gray prediction (GP); Neural stem cells
First Page Preview
Automatic tracking of neural stem cells in sequential digital images
Publisher
Database: Elsevier - ScienceDirect
Journal: Biocybernetics and Biomedical Engineering - Volume 36, Issue 1, 2016, Pages 66–75
Authors
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Subjects
Physical Sciences and Engineering Chemical Engineering Bioengineering