Automatic tracking of neural stem cells in sequential digital images

• Autorzy: Zhang T. , Jia W. , Zhu Y. , Yang J.

Identyfikatory

DOI

10.1016/j.bbe.2015.10.001

• Języki publikacji: EN

Abstrakty

EN

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.

Słowa kluczowe

EN

cell tracking; improved mean shift; dynamic local prediction; gray prediction; neural stem cells

PL

śledzenie komórki; lokalna prognoza dynamiczna; komórka macierzysta

• Wydawca: Nałęcz Institute of Biocybernetics and Biomedical Engineering of the Polish Academy of Sciences ; Elsevier
• Czasopismo: Biocybernetics and Biomedical Engineering
• Rocznik: 2016
• Tom: Vol. 36, no. 1
• Strony: 66--75
• Opis fizyczny: Bibliogr. 36 poz., rys., tab., wykr.

Twórcy

autor

Zhang T.

• Institute of Image Processing and Pattern Recognition, Shanghai Jiao Tong University, Shanghai, China

autor

Jia W.

• Faculty of Engineering and Information Technology, University of Technology, Australia

autor

Zhu Y.

• Centre National de la Recherche Scientifique of France, Villeurbanne, France

autor

Yang J.

• Institute of Image Processing and Pattern Recognition, Shanghai Jiao Tong University, Shanghai, China

Bibliografia

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Uwagi

PL

Opracowanie ze środków MNiSW w ramach umowy 812/P-DUN/2016 na działalność upowszechniającą naukę.