SpringerLink
Log in

Micro-object motion tracking based on the probability hypothesis density particle tracker

  • Published:
Journal of Mathematical Biology Aims and scope Submit manuscript

Abstract

Tracking micro-objects in the noisy microscopy image sequences is important for the analysis of dynamic processes in biological objects. In this paper, an automated tracking framework is proposed to extract the trajectories of micro-objects. This framework uses a probability hypothesis density particle filtering (PF-PHD) tracker to implement a recursive state estimation and trajectories association. In order to increase the efficiency of this approach, an elliptical target model is presented to describe the micro-objects using shape parameters instead of point-like targets which may cause inaccurate tracking. A novel likelihood function, not only covering the spatiotemporal distance but also dealing with geometric shape function based on the Mahalanobis norm, is proposed to improve the accuracy of particle weight in the update process of the PF-PHD tracker. Using this framework, a larger number of tracks are obtained. The experiments are performed on simulated data of microtubule movements and real mouse stem cells. We compare the PF-PHD tracker with the nearest neighbor method and the multiple hypothesis tracking method. Our PF-PHD tracker can simultaneously track hundreds of micro-objects in the microscopy image sequence.

This is a preview of subscription content, log in via an institution to check access.

Access this article

Log in via an institution

Subscribe and save

Springer+ Basic
EUR 32.99 /Month
  • Get 10 units per month
  • Download Article/Chapter or Ebook
  • 1 Unit = 1 Article or 1 Chapter
  • Cancel anytime
Subscribe now

Buy Now

Price excludes VAT (USA)
Tax calculation will be finalised during checkout.

Instant access to the full article PDF.

Institutional subscriptions

Fig. 1
Fig. 2
Fig. 3
Fig. 4
Fig. 5
Fig. 6
Fig. 7
Fig. 8
Fig. 9
Fig. 10
Fig. 11
Fig. 12

Similar content being viewed by others

Notes

  1. For the reader’s scrutiny, the track output for 100 frames is performed with a video in slow motion at http://sse.hit.edu.cn/wp-content/uploads/stepts100.rar.

  2. http://www.codesolorzano.com/celltrackingchallenge/Cell_Tracking_Challenge/Datasets.html.

References

  • Agaian S, Panetta K, Grigoryan AM (2001) Transform-based image enhancement algorithms with performance measure. IEEE Trans Image Process 10(3):367–382

    Article  MATH  Google Scholar 

  • Bengtsson T, Bickel P, Li B (2008) Curse-of-dimensionality revisited: Collapse of the particle filter in very large scale systems. Probability and statistics: essays in honor of David A. Freedman. IMS Collections, pp 316–334

  • Blackman S (2004) Multiple hypothesis tracking for multiple target tracking. IEEE Aerosp Electron Syst Mag 19(1):5–18

    Article  Google Scholar 

  • Bonneau S, Dahan M, Cohen LD (2005) Single quantum dot tracking based on perceptual grou** using minimal paths in a spatiotemporal volume. IEEE Trans Image Process 14(9):1384–1395

    Article  Google Scholar 

  • Buades A, Coll B, Morel JM (2010) Image denoising methods: a new nonlocal principle. SIAM Rev 52(1):113–147

    Article  MathSciNet  MATH  Google Scholar 

  • Buehren M (2014) http://www.mathworks.com/matlabcentral/fileexchange/6543

  • Chatterjee R, Ghosh M, Chowdhury AS, Ray N (2013) Cell tracking in microscopic video using matching and linking of bipartite graphs. Comput Meth Prog Bio 112(3):422–431

    Article  Google Scholar 

  • Chen G, Qian SE (2011) Denoising of hyperspectral imagery using principal component analysis and wavelet shrinkage. IEEE Trans Geosci Remote Sens 49(3):973–980

    Article  Google Scholar 

  • Chenouard N, Bloch I, Olivo-Marin JC (2009) Multiple hypothesis tracking in microscopy images. IEEE ISBI, pp 1346–1349

  • Chenouard N, Smal I, De Chaumont F, Maška M, Sbalzarini IF, Gong Y et al (2014) Objective comparison of particle tracking methods. Nat Methods 11(3):281–289

    Article  Google Scholar 

  • Clark DE, Bell J (2007) Multi-target state estimation and track continuity for the particle PHD filter. IEEE Trans Aerosp Electron Syst 43(4):1441–1453

    Article  Google Scholar 

  • Comaniciu D, Ramesh V, Meer P (2003) Kernel-based object tracking. IEEE Trans Pattern Anal Mach Intell 25(5):564–577

    Article  Google Scholar 

  • Daum F, Huang J (2011) Particle degeneracy: root cause and solution. Proc SPIE 8050:1–11

    Google Scholar 

  • Delande E, Uney M, Houssineau J et al (2014) Regional variance for multi-object filtering. IEEE Trans Sig Proc 62(13):3415–3428

    Article  MathSciNet  Google Scholar 

  • Frischknecht F, Baldacci P, Martin B, Zimmer C, Thiberge S, Olivo-Marin JC, Shorte SL, Ménard R (2004) Imaging movement of malaria parasites during transmission by anopheles mosquitoes. Cell Microbiol 6(7):687–694

    Article  Google Scholar 

  • Geerts H, De Brabander M, Nuydens R, Geuens S et al (1987) Nanovid tracking: a new automatic method for the study of mobility in living cells based on colloidal gold and video microscopy. Biophysical 52(5):775–782

    Article  Google Scholar 

  • Genovesio A, Liedl T, Emiliani V, Parak WJ, Coppey M, Olivo JC (2006) Multiple particle tracking in \(3d+ t\) microscopy: method and application to the tracking of endocytosed quantum dots. IEEE Trans Image Process 15(5):1062–1070

    Article  Google Scholar 

  • Hand A, Sun T, Barber D, Hose D, MacNeil S (2009) Automated tracking of migrating cells in phase-contrast video microscopy sequences using image registration. J Microsc 234(1):62–79

    Article  MathSciNet  Google Scholar 

  • Horn BK, Schunck BG (1981) Determining optical flow. In: 1981 Technical Symposium East, International Society for Optics and Photonics, pp 319–331

  • Jaqaman K, Loerke D, Mettlen M, Kuwata H, Grinstein S, Schmid SL, Danuser G (2008) Robust single-particle tracking in live-cell time-lapse sequences. Nat methods 5(8):695–702

    Article  Google Scholar 

  • Khan ZH, Gu IYH, Backhouse AG (2011) Robust visual object tracking using multi-mode anisotropic mean shift and particle filters. IEEE Trans Circ Syst Video Technol 21(1):74–87

    Article  Google Scholar 

  • Kimori Y (2011) Mathematical morphology-based approach to the enhancement of morphological features in medical images. J Clin Bioinform 1(1):1–10

    Article  Google Scholar 

  • Koch JW (2008) Bayesian approach to extended object and cluster tracking using random matrices. IEEE Trans Aerosp Electron Syst 44(3):1042–1059

    Article  Google Scholar 

  • Maggio E, Taj M, Cavallaro A (2008) Efficient multitarget visual tracking using random finite sets. IEEE Trans Circ Syst Video Technol 18(8):1016–1027

    Article  Google Scholar 

  • Mahler RP (2003) Multitarget bayes filtering via first-order multitarget moments. IEEE Trans Aerosp Electron Syst 39(4):1152–1178

    Article  Google Scholar 

  • Mahler RP (2007) Statistical multisource-multitarget information fusion. Artech House Inc, Norwood

    MATH  Google Scholar 

  • Mahler R (2007) PHD filters of higher order in target number. IEEE Trans Aerosp Electron Syst 43(4):1523–1543

    Article  Google Scholar 

  • McDonald D, Vodicka MA, Lucero G et al (2002) Visualization of the intracellular behavior of hiv in living cells. J Cell Biol 159(3):441–452

    Article  Google Scholar 

  • Meijering E, Dzyubachyk O, Smal I et al (2009) Tracking in cell and developmental biology. Semin Cell Dev Biol 20(8):894–902

    Article  Google Scholar 

  • Meijering E, Dzyubachyk O, Smal I et al (2012) Methods for cell and particle tracking. Methods Enzymol 504(9):183–200

    Article  Google Scholar 

  • Miura K (2005) Tracking movement in cell biology. In: Microscopy techniques. Springer, New York, pp 267–295

  • Nabeshima T, Inoue S, Okamoto K, Posadas-Herrera G, Yu F, Uchida L, Ichinose A et al (2014) Tanay virus, a new species of virus isolated from mosquitoes in the philippines. J Gen Virol 95(6):1390–1395

    Article  Google Scholar 

  • Ouellette NT, Xu H, Bodenschatz E (2006) A quantitative study of three-dimensional Lagrangian particle tracking algorithms. Exp Fluids 40(2):301–313

    Article  Google Scholar 

  • Panta K, Vo BN, Singh S et al (2004) Probability hypothesis density filter versus multiple hypothesis tracking. SPIE.DSS, pp 284–295

  • Panta K, Clark DE, Vo BN (2009) Data association and track management for the gaussian mixture probability hypothesis density filter. IEEE Trans Aerosp Electron Syst 45(3):1003–1016

    Article  Google Scholar 

  • Paragios N, Deriche R (2000) Geodesic active contours and level sets for the detection and tracking of moving objects. IEEE Trans Pattern Anal Mach Intell 22(3):266–280

    Article  Google Scholar 

  • Parrilli S, Poderico M, Angelino CV, Verdoliva L (2012) A nonlocal sar image denoising algorithm based on llmmse wavelet shrinkage. IEEE Trans Geosci Remote Sens 50(2):606–616

    Article  Google Scholar 

  • Ristic B, Clark D, Vo BN (2010) Improved SMC implementation of the PHD filter. FUSION, pp 1–8

  • Ristic B, Vo BN, Clark D, Vo BT (2011) A metric for performance evaluation of multi-target tracking algorithms. IEEE Trans Sig Process 59(7):3452–3457

    Article  MathSciNet  Google Scholar 

  • Rosser G, Fletcher AG, Wilkinson DA et al (2013) Novel methods for analysing bacterial tracks reveal persistence in Rhodobacter sphaeroides. PLoS Comput Biol 9(10):e1003276

    Article  Google Scholar 

  • Schuhmacher D, Vo BT, Vo BN (2008) A consistent metric for performance evaluation of multi-object filters. IEEE Trans Sig Process 56(8):3447–3457

    Article  MathSciNet  Google Scholar 

  • Shan C, Wei Y, Tan T et al (2004) Real time hand tracking by combining particle filtering and mean shift. FGR, pp 669–674

  • Smal I, Niessen W, Meijering E (2008) A new detection scheme for multiple object tracking in fluorescence microscopy by joint probabilistic data association filtering. I S Biomed Imaging, pp 264–267

  • Snyder D, Miller M (1991) Random point processes in time and space. Springer, New York, pp 113–165

    MATH  Google Scholar 

  • Snyder C, Bengtsson T, Bickel P et al (2008) Obstacles to high-dimensional particle filtering. Mon Weather Rev 136(12):4629–4640

    Article  Google Scholar 

  • Streit R (2009) PHD intensity filtering is one step of a MAP estimation algorithm for positron emission tomography. IEEE FUSION’09, pp 308–315

  • Streit R (2010) Poisson point processes: imaging, tracking, and sensing. Springer, New York

    Book  Google Scholar 

  • Streit R (2013) The probability generating functional for finite point processes, and its application to the comparison of PHD and intensity filters. J Adv Inform Fusion 8(2):119–132

    MathSciNet  Google Scholar 

  • Tinevez JY (2011)http://www.mathworks.com/matlabcentral/fileexchange/33772-nearest-neighbor-linker/content/nearestneighborlinker.m

  • Veenman C, Reinders M, Backer A (2003) Motion tracking as a constrained optimization problem. Pattern Recogn 36(9):2049–2067

    Article  MATH  Google Scholar 

  • Vo BN, Ma WK (2006) The Gaussian mixture probability hypothesis density filter. IEEE Trans Sig Process 54(11):4091–4104

    Article  Google Scholar 

  • Wang YD, Wu JK, Kassim AA, Huang W (2008) Data-driven probability hypothesis density filter for visual tracking. Circ Syst Video Technol 18(8):1085–1095

    Article  Google Scholar 

  • Wood TM, Yates CA, Wilkinson DA, Rosser G (2012) Simplified multitarget tracking using the phd filter for microscopic video data. Circ Syst Video Technol 22(5):702–713

    Article  Google Scholar 

  • Yang L, Qiu Z, Greenaway AH et al (2012) A new framework for particle detection in low-SNR fluorescence live-cell images and its application for improved particle tracking. IEEE Trans Biomed Eng 59(7):2040–2050

    Article  Google Scholar 

  • Yilmaz A, Javed O, Shah M (2006) Object tracking: a survey. ACM Comput Surv 38(4):13

    Article  Google Scholar 

  • Yuan L, Zheng YF, Zhu J, Wang L, Brown A (2012) Object tracking with particle filtering in fluorescence microscopy images: application to the motion of neurofilaments in axons. IEEE Trans Med Imag 31(1):117–130

    Article  Google Scholar 

  • Zhang L, Li Y, Nevatia R (2008) Global data association for multi-object tracking using network flows. CVPR IEEE, pp 1–8

  • Zhao L, Ma P, Su X, Zhang H (2010) A new multi-target state estimation algorithm for phd particle filter. FUSION, pp 1–8

  • Zimmer C, Labruyere E, Meas-Yedid V, Guillen N, Olivo-Marin JC (2002) Segmentation and tracking of migrating cells in videomicroscopy with parametric active contours: a tool for cell-based drug testing. IEEE Trans Med Imag 21(10):1212–1221

    Article  Google Scholar 

Download references

Acknowledgments

We thank the anonymous reviewers for their insightful comments and suggestions.

Author information

Authors and Affiliations

  1. Department of Mathematics, Harbin Institute of Technology, Harbin, 150001, China

    Chunmei Shi & Chi** Zhang

  2. School of Computer Science and Technology, Harbin Institute of Technology, Harbin, 150001, China

    Lingling Zhao, Junjie Wang, **aohong Su & Peijun Ma

Authors
  1. Chunmei Shi
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Lingling Zhao
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Junjie Wang
    View author publications

    You can also search for this author in PubMed Google Scholar

  4. Chi** Zhang
    View author publications

    You can also search for this author in PubMed Google Scholar

  5. **aohong Su
    View author publications

    You can also search for this author in PubMed Google Scholar

  6. Peijun Ma
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Chunmei Shi.

Additional information

This paper was supported by the National Natural Science Foundation of China (NSFC) under Grant 61175027, 61305013 and the Fundamental Research Funds for the Central Universities (Grant No. HIT.NSRIF.2014071), Research Fund for the Doctoral Program of Higher Education of China (No. 20132302120044).

Rights and permissions

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Shi, C., Zhao, L., Wang, J. et al. Micro-object motion tracking based on the probability hypothesis density particle tracker. J. Math. Biol. 72, 1225–1254 (2016). https://doi.org/10.1007/s00285-015-0909-9

Download citation

  • Received:

  • Revised:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s00285-015-0909-9

Keywords

Mathematics Subject Classification

Search

Navigation