Abstract
This paper proposes an automatic cell cycle localization method based on the Latent-Dynamic Conditional Random Fields (LDCRFs) model. Since the LDCRFs model can jointly capture both extrinsic dynamics and intrinsic sub-structure, it can simultaneously model the visual dynamics within one stage and visual transition between adjacent stages in one mitosis sequence. Based on our previous work on candidate mitosis sequence extraction and classification, this paper mainly focuses on the formulation of LDCRFs for cell cycle modeling. Besides, the model learning and inference methods are also presented. The evaluation on C2C12 dataset shows the superiority of the proposed method.
This is a preview of subscription content, log in via an institution to check access.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
References
Agrawal P, Vatsa M, Singh R (2013) Hep-2 cell image classification: a comparative analysis. In: Machine learning in medical imaging. Springer, pp 195–202
Becker T, Madany A (2012) Morphology-based features for adaptive mitosis detection of in vitro stem cell tracking data. Methods Inf Med 51(5):449
Kumar S, Hebert M (2003) Discriminative random fields: a discriminative framework for contextual interaction in classification. In: Proceedings of 9th IEEE international conference on computer vision, 2003. IEEE, pp 1150–1157
Liang L, Zhou X, Li F, Wong ST, Huckins J, King RW (2007) Mitosis cell identification with conditional random fields. In: Life science systems and applications workshop, 2007 (LISA 2007). IEEE/NIH, pp 9–12
Liu AA, Li K, Kanade T (2010) Mitosis sequence detection using hidden conditional random fields. In: 2010 I.E. international symposium on biomedical imaging: from nano to macro. IEEE, pp 580–583
Liu AA, Li K, Kanade T (2012) A semi-markov model for mitosis segmentation in time-lapse phase contrast microscopy image sequences of stem cell populations. IEEE Trans Med Imaging 31(2):359–369
Morency L, Quattoni A, Darrell T (2007) Latent-dynamic discriminative models for continuous gesture recognition. In: IEEE conference on computer vision and pattern recognition, 2007 (CVPR’07). IEEE, pp 1–8
Oliva A, Torralba A (2001) Modeling the shape of the scene: a holistic representation of the spatial envelope. Int J Comput Vis 42(3):145–175
Quattoni A, Wang S, Morency LP, Collins M, Darrell T, Csail M (2007) Hidden-state conditional random fields. IEEE Trans Pattern Anal Mach Intell 29(10):1848–1852
Siva P, Brodland GW, Clausi D (2007) Automated detection of mitosis in embryonic tissues. In: Fourth Canadian conference on computer and robot vision, 2007 (CRV’07). IEEE, pp 97–104
Thirusittampalam K, Hossain J, Whelan PF (2013) A novel framework for cellular tracking and mitosis detection in dense phase contrast microscopy images. IEEE Trans Biomed Eng 17(3):642–653
Tripathi AS, Mathur A, Daga M, Kuse M, Au OC (2013) 2-simdom: a 2-sieve model for detection of mitosis in multispectral breast cancer imagery. In: International conference on image processing. IEEE
Wiliem A, Sanderson C, Wong Y, Hobson P, Minchin RF, Lovell BC (2013) Automatic classification of human epithelial type 2 cell indirect immunofluorescence images using cell pyramid matching. Pattern Recognit
Acknowledgements
This work was supported in part by the National Natural Science Foundation of China (61100124, 21106095, 61170239, and 61202168), the grant of Elite Scholar Program of Tianjin University, the grant of Introducing Talents to Tianjin Normal University (5RL123), the grant of Introduction of One Thousand High-level Talents in Three Years in Tianjin.
Author information
Authors and Affiliations
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2015 Springer International Publishing Switzerland
About this paper
Cite this paper
Zhang, J. et al. (2015). Automatic Cell Cycle Localization Using Latent-Dynamic Conditional Random Fields. In: Mu, J., Liang, Q., Wang, W., Zhang, B., Pi, Y. (eds) The Proceedings of the Third International Conference on Communications, Signal Processing, and Systems. Lecture Notes in Electrical Engineering, vol 322. Springer, Cham. https://doi.org/10.1007/978-3-319-08991-1_57
Download citation
DOI: https://doi.org/10.1007/978-3-319-08991-1_57
Publisher Name: Springer, Cham
Print ISBN: 978-3-319-08990-4
Online ISBN: 978-3-319-08991-1
eBook Packages: EngineeringEngineering (R0)