TY - GEN
T1 - Compressive tracking moving cells in time-lapse image sequences
AU - Ding, Chen
AU - Li, Ying
AU - Pan, Yongsheng
AU - Zhou, Tao
AU - Gao, Pengcheng
AU - Xia, Yong
N1 - Publisher Copyright:
© 2015 IEEE.
PY - 2016/6/22
Y1 - 2016/6/22
N2 - Tracking the motion of cells in time-lapse image sequences plays a pivotal role in both research settings and clinical practices. In spite of their prevalence, automated cell tracking approaches are still facing several major challenges, including the effectiveness of cell detection, accuracy of tracking and high computational complexity. In this paper, we propose a segmentation-based compressive tracking (SBCT) algorithm for moving cells. This algorithm consists three major steps, including detecting the bounding box of each cell, extracting image features in each bounding box using compressive sensing, and identifying the correspondence between cells in adjacent frames using a trained naive Bayes classifier. The proposed SBCT algorithm has been evaluated against seven state-of-the-art cell tracking approaches on two time-lapse images sequences provided by the 2014 cell tracking challenge. Our results suggest that the proposed algorithm can successfully tracking moving cells with relatively high accuracy and low computational complexity.
AB - Tracking the motion of cells in time-lapse image sequences plays a pivotal role in both research settings and clinical practices. In spite of their prevalence, automated cell tracking approaches are still facing several major challenges, including the effectiveness of cell detection, accuracy of tracking and high computational complexity. In this paper, we propose a segmentation-based compressive tracking (SBCT) algorithm for moving cells. This algorithm consists three major steps, including detecting the bounding box of each cell, extracting image features in each bounding box using compressive sensing, and identifying the correspondence between cells in adjacent frames using a trained naive Bayes classifier. The proposed SBCT algorithm has been evaluated against seven state-of-the-art cell tracking approaches on two time-lapse images sequences provided by the 2014 cell tracking challenge. Our results suggest that the proposed algorithm can successfully tracking moving cells with relatively high accuracy and low computational complexity.
KW - Cell tracking
KW - compressive sensing
KW - image segmentation
KW - naive Bayes classifier
UR - http://www.scopus.com/inward/record.url?scp=84980318053&partnerID=8YFLogxK
U2 - 10.1109/ICOT.2015.7498479
DO - 10.1109/ICOT.2015.7498479
M3 - 会议稿件
AN - SCOPUS:84980318053
T3 - Proceedings of 2015 International Conference on Orange Technologies, ICOT 2015
SP - 75
EP - 78
BT - Proceedings of 2015 International Conference on Orange Technologies, ICOT 2015
PB - Institute of Electrical and Electronics Engineers Inc.
T2 - 3rd International Conference on Orange Technologies, ICOT 2015
Y2 - 19 December 2015 through 22 December 2015
ER -