Plant cell tracking using Kalman filter based local graph matching

Abstract Automated tracking of cells in time lapse live-imaging datasets of developing multicellular tissues is required for high throughput spatio-temporal quantitative measurements of a range of cell behaviors, such as cell division, migration and cell growth. In this paper, a Kalman filter based local graph matching method is proposed to track the plant cells, by exploiting the tight spatial topology of neighboring cells in a multicellular field as contextual information. The Kalman filter is used to predict the movement of the cells, and then the local graph matching approach is used to search the target cells in the neighborhood of the predicted position. The combination of the Kalman filter and local graph matching greatly reduces the size of the searching region in the matching process and enhances the tracking stability as well. Furthermore, the cells' lineage tracklets could be associated by using the cells' spatial–temporal contextual information to obtain long-term lineages. Finally, we proposed a graph evolution method to enhance the association robustness by considering the statistical properties of individual cell tracklets. The effectiveness and efficiency of the proposed tracking method are validated by experiments on real datasets.

[1]  Christophe Zimmer,et al.  Segmenting and tracking fluorescent cells in dynamic 3-D microscopy with coupled active surfaces , 2005, IEEE Transactions on Image Processing.

[2]  Amit K. Roy-Chowdhury,et al.  Context aware spatio-temporal cell tracking in densely packed multilayer tissues , 2015, Medical Image Anal..

[3]  Philipp J. Keller,et al.  Fast, accurate reconstruction of cell lineages from large-scale fluorescence microscopy data , 2014, Nature Methods.

[4]  A. Roy-Chowdhury,et al.  Automated tracking of stem cell lineages of Arabidopsis shoot apex using local graph matching. , 2010, The Plant journal : for cell and molecular biology.

[5]  Anirban Chakraborty,et al.  Adaptive cell segmentation and tracking for volumetric confocal microscopy images of a developing plant meristem. , 2011, Molecular plant.

[6]  G. Malandain,et al.  Imaging plant growth in 4D: robust tissue reconstruction and lineaging at cell resolution , 2010, Nature Methods.

[7]  Gérard G. Medioni,et al.  Multiple Target Tracking Using Spatio-Temporal Markov Chain Monte Carlo Data Association , 2007, 2007 IEEE Conference on Computer Vision and Pattern Recognition.

[8]  Bi Song,et al.  A Stochastic Graph Evolution Framework for Robust Multi-target Tracking , 2010, ECCV.

[9]  Anil K. Jain,et al.  A modified Hausdorff distance for object matching , 1994, Proceedings of 12th International Conference on Pattern Recognition.

[10]  Yuhui Zheng,et al.  Image segmentation by generalized hierarchical fuzzy C-means algorithm , 2015, J. Intell. Fuzzy Syst..

[11]  Takeo Kanade,et al.  Cell population tracking and lineage construction with spatiotemporal context , 2008, Medical Image Anal..

[12]  Naixue Xiong,et al.  Steganalysis of LSB matching using differences between nonadjacent pixels , 2016, Multimedia Tools and Applications.

[13]  Paul A. Viola,et al.  Alignment by Maximization of Mutual Information , 1997, International Journal of Computer Vision.

[14]  Mrinal Kumar,et al.  An MCMC-based particle filter for multiple target tracking , 2012, 2012 15th International Conference on Information Fusion.

[15]  Abdullahi Daniyan.  Performance Analysis of Sequential Monte Carlo MCMC and PHD Filters on Multi-target Tracking in Video , 2014, 2014 European Modelling Symposium.

[16]  Subramanian Ramanathan,et al.  Multitask Linear Discriminant Analysis for View Invariant Action Recognition , 2014, IEEE Transactions on Image Processing.

[17]  Xingming Sun,et al.  Segmentation-Based Image Copy-Move Forgery Detection Scheme , 2015, IEEE Transactions on Information Forensics and Security.

[18]  Narendra Ahuja,et al.  Robust multi-object tracking via cross-domain contextual information for sports video analysis , 2012, 2012 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP).

[19]  Hong Yan,et al.  A Probabilistic Relaxation Labeling (PRL) Based Method for C. elegans Cell Tracking in Microscopic Image Sequences , 2016, IEEE Journal of Selected Topics in Signal Processing.

[20]  Ong.  Tracking of cell population from time lapse and end point confocal microscopy images with multiple hypothesis Kalman smoothing filters , 2010 .

[21]  Jiwen Lu,et al.  Deep Metric Learning for Visual Tracking , 2016, IEEE Transactions on Circuits and Systems for Video Technology.

[22]  Eric Mjolsness,et al.  Tracking Cell Signals in Fluorescent Images , 2005, 2005 IEEE Computer Society Conference on Computer Vision and Pattern Recognition (CVPR'05) - Workshops.

[23]  Amit K. Roy-Chowdhury,et al.  Efficient cell segmentation and tracking of developing plant meristem , 2011, 2011 18th IEEE International Conference on Image Processing.

[24]  Anand Rangarajan,et al.  A new algorithm for non-rigid point matching , 2000, Proceedings IEEE Conference on Computer Vision and Pattern Recognition. CVPR 2000 (Cat. No.PR00662).

[25]  Jens Rittscher,et al.  Spatio-temporal cell cycle phase analysis using level sets and fast marching methods , 2009, Medical Image Anal..

[26]  Wiro J. Niessen,et al.  Advanced Level-Set-Based Cell Tracking in Time-Lapse Fluorescence Microscopy , 2010, IEEE Transactions on Medical Imaging.

[27]  Long Chen,et al.  A novel cell nuclei segmentation method for 3D C. elegans embryonic time-lapse images , 2013, BMC Bioinformatics.

[28]  Anthony Santella,et al.  A semi-local neighborhood-based framework for probabilistic cell lineage tracing , 2014, BMC Bioinformatics.

[29]  David G. Stork,et al.  Pattern Classification , 1973 .

[30]  Gang Wang,et al.  Human Identity and Gender Recognition From Gait Sequences With Arbitrary Walking Directions , 2014, IEEE Transactions on Information Forensics and Security.

[31]  Joakim Jalden,et al.  Global Linking of Cell Tracks Using the Viterbi Algorithm , 2015, IEEE Transactions on Medical Imaging.

[32]  Shaogang Gong,et al.  Quantifying and Transferring Contextual Information in Object Detection , 2012, IEEE Transactions on Pattern Analysis and Machine Intelligence.