@inproceedings{240f2a78d33c493dbfd8f797123e02c1,
title = "Characterization of single cell dynamic morphology by local deformation pattern modeling",
abstract = "Computational analysis of cell dynamic morphology in time-lapse images has become a new topic of biomedical research. For single cell, it is a challenging task to consider the spatial inconsistency and the temporal accumulation of cell deformation. This paper introduces an innovative automate analysis method, in which temporal features of contour point deformation are captured and then local deformation pattern is modeled to characterize cell dynamic morphology and predict cell activation statue. We applied the method to classify lymphocyte videos of multiple groups. Experimental results demonstrate that the proposed method overcomes existing methods in accuracy and robustness.",
keywords = "Cell dynamic morphology, Local deformation pattern, Temporal feature, Time-lapse image",
author = "Heng Li and Zhiwen Liu and Fengqian Pang and Yonggang Shi",
note = "Publisher Copyright: {\textcopyright} 2017 IEEE.; 39th Annual International Conference of the IEEE Engineering in Medicine and Biology Society, EMBC 2017 ; Conference date: 11-07-2017 Through 15-07-2017",
year = "2017",
month = sep,
day = "13",
doi = "10.1109/EMBC.2017.8036829",
language = "English",
series = "Proceedings of the Annual International Conference of the IEEE Engineering in Medicine and Biology Society, EMBS",
publisher = "Institute of Electrical and Electronics Engineers Inc.",
pages = "329--332",
booktitle = "2017 39th Annual International Conference of the IEEE Engineering in Medicine and Biology Society",
address = "United States",
}