Tracking of neural stem cells in high density image sequence based on Topological constraint combined with Hungarian algorithm / 生物医学工程学杂志
Journal of Biomedical Engineering
;
(6): 597-603, 2012.
Artigo
em Chinês
| WPRIM
| ID: wpr-271726
ABSTRACT
Analysis of neural stem cells' movements is one of the important parts in the fields of cellular and biological research. The main difficulty existing in cells' movement study is whether the cells tracking system can simultaneously track and analyze thousands of neural stem cells (NSCs) automatically. We present a novel cells' tracking algorithm which is based on segmentation and data association in this paper, aiming to improve the tracking accuracy further in high density NSCs' image. Firstly, we adopted different methods of segmentation base on the characteristics of the two cell image sequences in our experiment. Then we formed a data association and constituted a coefficient matrix by all cells between two adjacent frames according to topological constraints. Finally we applied The Hungarian algorithm to implement inter-cells matching optimally. Cells' tracking can be achieved according to this model from the second frame to the last one in a sequence. Experimental results showed that this approaching method has higher accuracy compared with that using the topological constraints tracking alone. The final tracking accuracies of average of sequence I and sequence II have been improved 10.17% and 4%, respectively.
Texto completo:
DisponíveL
Índice:
WPRIM (Pacífico Ocidental)
Assunto principal:
Algoritmos
/
Processamento de Imagem Assistida por Computador
/
Contagem de Células
/
Movimento Celular
/
Biologia Celular
/
Células-Tronco Neurais
/
Rastreamento de Células
/
Métodos
/
Microscopia de Fluorescência
/
Modelos Teóricos
Tipo de estudo:
Estudo prognóstico
Limite:
Animais
Idioma:
Chinês
Revista:
Journal of Biomedical Engineering
Ano de publicação:
2012
Tipo de documento:
Artigo
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