A New Method for Characterization of Biological Particles in Microscopic Videos: Hypothesis Testing Based on a Combination of Stochastic Modeling and Graph Theory

Document Type : Original Paper

10.22038/ijmp.2012.154

Abstract

Introduction
Studying motility of biological objects is an important parameter in many biomedical processes. Therefore, automated analyzing methods via microscopic videos are becoming an important step in recent researches.
Materials and Methods
In the proposed method of this article, a hypothesis testing function is defined to separate biological particles from artifact and noise in captured video. Then, a decision about each hypothesis is made in the following steps: selecting primary candidates using stochastic modeling, pruning false candidates using graph theory, and confirming remained particles by Kalman filtering.
Results
Performance of the proposed method is evaluated on real videos containing low and high densities of live Listeria particles. The results show that in the first scenario, the proposed and MD algorithms detect 95% and 65% of particles in presence of 2% and 44% false detections, respectively. In the second scenario, the proposed and MD algorithms detect 91% and 55% of particles in presence of 14% and 45% false detections, respectively.
Conclusion
In the first scenario, the proposed algorithm detects and tracks particles typically 30% and 31% better than MD. Moreover, its false detected particles and trajectories are 42% and 27% less than MD. In the second scenario, the proposed method detects and tracks particles typically 36% and 38% better than MD, also its false detected particles and trajectories are 31% and 18% less than MD. Consequently, better characterization of particles in proposed algorithm not only does not lead to extracting more false particles and trajectories but also decreases the rate of false characterized particles and trajectories compared with existing algorithms.

Keywords

Main Subjects


  1. Lenz P. Cell Motility: Springer; 2008.
  2. Acton S, Ley K. Tracking leukocytes from in vivo video microscopy using morphological anisotropic diffusion. Proc Int Conf Image Proc. 2001 Oct 2: 300-3.
  3. Xie J, Khan S, Shah M. Automatic tracking of Escherichia coli in phase-contrast microscopy video. IEEE Trans Biomed Eng. 2009 Feb;56(2):390-9.
  4. Milović NM, Wang J, Lewis K, Klibanov AM. Immobilized N-alkylated polyethylenimine avidly kills bacteria by rupturing cell membranes with no resistance developed. Biotechnol Bioeng. 2005 Jun 20;90(6):715-22.
  5. Min L, Roy-Chowdhury AK, Venugopala Reddy G, editors. Robust estimation of stem cell lineages using local graph matching. Computer Vision and   Pattern Recognition Workshops, 2009 CVPR Workshops 2009 IEEE Computer Society Conference on; 2009 20-25 June 2009.
  6. Paduano V, Sepe L, Cantarella C, Sansone C, Paolella G, Ceccarelli M, editors. Time-lapse phase-contrast microscopy fibroblast automated tracking. Imaging Systems and Techniques (IST), 2010 IEEE International Conference on; 2010 1-2 July 2010.
  7. Zheng Li X, Wang Zhi Y, editors. The sperm video segmentation based on dynamic threshold. Machine Learning and Cybernetics (ICMLC), 2010 International Conference on; 2010 11-14 July 2010.

 

  1. Wenzhong Y, Shuqun S. Automatic Chromosome Counting Algorithm Based on Mathematical Morphology. Journal of Data Acquisition & Processing. 2008;23(9):1004-9037.
  2. Wu Q, Merchant F, Castleman KR. Microscope Image Processing: Elsevier/Academic Press; 2008.
  3. Goobic AP, Welser ME, Acton ST, Ley K, editors. Biomedical application of target tracking in clutter. Signals, Systems and Computers, 2001 Conference Record of the Thirty-Fifth Asilomar Conference on; 2001 4-7 Nov. 2001.
  4. Menkveld R, Kruger T. Evaluation of sperm morphology by light microscopy, Assisted Reproduction. 2nd Edition, Partheon Publishing Group, 1996.
  5. Chowdhury AS, Chatterjee R, Ghosh M, Ray N, editors. Cell Tracking in Video Microscopy Using Bipartite Graph Matching. Pattern Recognition (ICPR), 2010 20th International Conference on; 2010 23-26 Aug. 2010.
  6. Wählby C, SINTORN IM, Erlandsson F, Borgefors G, Bengtsson E. Combining intensity, edge and shape information for 2D and 3D segmentation of cell nuclei in tissue sections. Journal of Microscopy. 2004;215(1):67-76.
  7. Schütz GJ, Schindler H, Schmidt T. Single-molecule microscopy on model membranes reveals anomalous diffusion. Biophys J. 1997 Aug;73(2):1073-80.
  8. Xinyu L, Yifei W, Yu S. Cell Contour Tracking and Data Synchronization for Real-Time, High-Accuracy Micropipette Aspiration. Automation Science and Engineering, IEEE Transactions on. 2009;6(3):536-43.
  9. Xiaodong Y, Houqiang L, Xiaobo Z, Wong S, editors. Automated segmentation and tracking of cells in time-lapse microscopy using watershed and mean shift. Intelligent Signal Processing and Communication Systems, 2005 ISPACS 2005 Proceedings of 2005 International Symposium on; 2005 13-16 Dec. 2005.
  10. Bhaskar H, Kingsland RL, Singh S, Welsh G, Tavare J, editors. Multiple Cell-Particle Tracking Based on Multi-Resolution Block-Matching using Genetic Algorithm. Advances in Medical, Signal and Information Processing, 2006 MEDSIP 2006 IET 3rd International Conference On; 2006 17-19 July 2006.
  11. DeGroot MH. Probability and statistics: Addison-Wesley Pub. Co.; 1986.
  12. Myung IJ. Tutorial on maximum likelihood estimation. Journal of Mathematical Psychology. 2003;47(1):90-100.
  13. Shapiro, L. and Haralick, R. Computer and Robot Vision., Addison-Wesley Pub. Co.; 1992, pp. 28-48.
  14. Gusfield D, Irving RW. The Stable Marriage Problem: Structure and Algorithms: MIT Press; 1989.
  15. Kay SM. Fundamentals of Statistical Signal Processing, Volume 2: Detection Theory. Prentice Hall Signal Processing Series; 1998.
  16. Ryser ET, Marth EH. Listeria, listeriosis, and food safety: CRC; 2007.
  17. Sertel O, Kong J, Lozanski G, Catalyurek U, Saltz J. Computerized microscopic image analysis of follicular lymphoma. Proceedings of SPIE. 2008;6915:1-11.