TY - GEN
T1 - Analyzing dynamic cellular morphology in time-lapsed images enabled by cellular deformation pattern recognition
AU - Li, Heng
AU - Liu, Zhiwen
AU - Pang, Fengqian
AU - Fan, Zhiyi
AU - Shi, Yonggang
N1 - Publisher Copyright:
© 2015 IEEE.
PY - 2015/11/4
Y1 - 2015/11/4
N2 - Computational analysis of cellular morphology aims to provide quantitative information of the global organizational and physiological state of cells, and has long been a major topic of biomedical research. Instead of analyzing morphology of static cells, we concentrate on live-cell deformation in a period of time. According to our observation of dynamic cell behavior, we have assumed that the pattern of cellular deformation is relevant to the cellular state. Moreover, based on our assumption an innovative approach for characterizing the deformation pattern is described and applied into cell classification. After normalizing and aligning cell image sequences, we extract the continuity of deformation at each angle through time-lapse. Then the deformation pattern is given by the histogram of the continuity of deformation. Experimental results demonstrate that the cellular deformation pattern provided by our approach can be applied to discriminate cellular activation. In addition, the deformation pattern recognition makes remarkable progress in the classification of cells.
AB - Computational analysis of cellular morphology aims to provide quantitative information of the global organizational and physiological state of cells, and has long been a major topic of biomedical research. Instead of analyzing morphology of static cells, we concentrate on live-cell deformation in a period of time. According to our observation of dynamic cell behavior, we have assumed that the pattern of cellular deformation is relevant to the cellular state. Moreover, based on our assumption an innovative approach for characterizing the deformation pattern is described and applied into cell classification. After normalizing and aligning cell image sequences, we extract the continuity of deformation at each angle through time-lapse. Then the deformation pattern is given by the histogram of the continuity of deformation. Experimental results demonstrate that the cellular deformation pattern provided by our approach can be applied to discriminate cellular activation. In addition, the deformation pattern recognition makes remarkable progress in the classification of cells.
UR - https://www.scopus.com/pages/publications/84953247222
U2 - 10.1109/EMBC.2015.7320121
DO - 10.1109/EMBC.2015.7320121
M3 - Conference contribution
C2 - 26738021
AN - SCOPUS:84953247222
T3 - Proceedings of the Annual International Conference of the IEEE Engineering in Medicine and Biology Society, EMBS
SP - 7478
EP - 7481
BT - 2015 37th Annual International Conference of the IEEE Engineering in Medicine and Biology Society, EMBC 2015
PB - Institute of Electrical and Electronics Engineers Inc.
T2 - 37th Annual International Conference of the IEEE Engineering in Medicine and Biology Society, EMBC 2015
Y2 - 25 August 2015 through 29 August 2015
ER -