A method for cytometric image parameterization

Optics Express

Volumn 14, Issue 26, 2006, Pages 12720-12743

  Pilarski, Patrick M ^a   Backhouse, Christopher J ^a  

^a UNIVERSITY OF ALBERTA   (Canada)

Author keywords

[No Author keywords available]

Indexed keywords

CELL CULTURE; COMPUTATIONAL METHODS; INFORMATION ANALYSIS; LIGHT SCATTERING; NANOSTRUCTURED MATERIALS; PARAMETER ESTIMATION;

LASER SCATTERING IMAGES; MULTI-AGENT COMPUTATIONAL PIPELINE; SCATTERING OBJECTS;

IMAGE PROCESSING;

EID: 33845734096     PISSN: None     EISSN: 10944087     Source Type: Journal    
DOI: 10.1364/OE.14.012720     Document Type: Article

References (50)

1. K. Singh, C. Liu, C. Capjack, W. Rozmus, and C. J. Backhouse, "Analysis of Cellular Structure by Light Scattering Measurements in a New Cytometer Design Based on a Liquid-Core Waveguide," IEE Proc.-Nanobiotechnol. 151, 10-16 (2004).
2. K. Singh, X. Su, C. Liu, C. Capjack, W. Rozmus, and C. J. Backhouse, "A Miniaturized Wide-Angle 2D Cytometer," Cytometry A 69A, 307-315 (2006).
3. P. L. Gourley and R. K. Naviaux, "Optical Phenotyping of Human Mitochondria in a Biocavity Laser," IEEE J. Quantum Electron. 11, 818-826 (2005).
4. V. P. Maltsev, "Scanning flow cytometry for individual particle analysis," Rev. Sci. Instrum. 71, 243-255 (2000).
5. P. L. Gourley, "Biocavity laser for high-speed cell and tumour biology," J. Phys. D-Appl. Phys. 36, R228-R239 (2003).
6. P. Chacon, F. Moran, J. F. Diaz, E. Pantos, and J. M. Andreu, "Low-resolution structures of proteins in solution retrieved from X-ray scattering with a genetic algorithm," Biophys. J. 74, 2760-2775 (1998).
7. P. Chacon, J. F. Diaz, F. Moran, and J. M. Andreu, "Reconstruction of protein form with X-ray solution scattering and a genetic algorithm," J. Mol. Biol. 299, 1289-1302 (2000).
8. J. D. Watson and F. H. C. Crick, "Molecular Structure Of Nucleic Acids - A Structure For Deoxyribose Nucleic Acid," Nature 171, 737-738 (1953).
9. K. A. Sem'yanov, P. A. Tarasov, J. T. Soini, A. K. Petrov, and V. P. Maltsev, "Calibration-free method to determine the size and hemoglobin concentration of individual red blood cells from light scattering," Appl. Opt. 39, 5884-5889 (2000).
10. N. Ghosh, P. Buddhiwant, A. Uppal, K. Majumder, H. S. Patel, and P. K. Gupta, "Simultaneous determination of size and refractive index of red blood cells by light scattering measurements," Appl. Phys. Lett. 88, 084101 (3 pages) (2006).
11. Z. Ulanowski, Z. Wang, P. H. Kaye, and I. K. Ludlow, "Application of neural networks to the inverse scattering problem for spheres," Appl. Opt. 37, 4027-4033 (1998).
12. R. Drezek, A. Dunn, and R. Richards-Kortum, "Light scattering from cells: finite-difference time-domain simulations and goniometric measurements," Appl. Opt. 38, 3651-3661 (1999).
13. R. Drezek, A. Dunn, and R. Richards-Kortum, "A pulsed finite-difference time-domain (FDTD) method for calculating light scattering from biological cells over broad wavelength ranges," Opt. Express 6, 147-157 (2000), http://www.opticsinfobase.org/abstract.cfm?URI=oe-6-7-147.
14. C. Liu, C. E. Capjack, and W. Rozmus, "3-D simulation of light scattering from biological cells and cell differentiation," J. Biomed. Opt. 10, 014007 (12 pages) (2005).
15. K. Sem'yanov and V. P. Maltsev, "Analysis of sub-micron spherical particles using scanning flow cytometry," Part. Part. Syst. Charact. 17, 225-229 (2000).
16. N. Richard, M. Dojat, and C. Garbay, "Automated segmentation of human brain MR images using a multi-agent approach," Artif. Intell. Med. 30, 153-175 (2004).
17. J. Liu, Y. Y. Tang, and Y. C. Cao, "An evolutionary autonomous agents approach to image feature extraction," IEEE Trans. Evol. Comput. 1, 141-158 (1997).
18. M. Schmidt, "Automated Brain Tumor Segmentation," Ph.D. thesis, University of Alberta (2005).
19. C. E. Priebe, D. J. Marchette, and G. W. Rogers, "Segmentation of random fields via borrowed strength density estimation," IEEE Trans. Pattern Anal. Mach. Intell. 19, 494-499 (1997).
20. E. G. P. Bovenkamp, J. Dijkstra, J. G. Bosch, and J. H. C. Reiber, "Multi-agent segmentation of IVUS images," Pattern Recogn. 37, 647-663 (2004).
21. E. Duchesnay, J. J. Montois, and Y. Jacquelet, "Cooperative agents society organized as an irregular pyramid: A mammography segmentation application," Pattern Recogn. Lett. 24, 2435-2445 (2003).
22. M. P. Wachowiak, R. Smolikova, Y. F. Zheng, J. M. Zurada, and A. S. Elmaghraby, "An approach to multimodal biomedical image registration utilizing particle swarm optimization," IEEE Trans. Evol. Comput. 8, 289-301 (2004).
23. N. Pal and S. Pal, "A review on image segmentation techniques," Pattern Recognit. 26, 1277-1294 (1993).
24. L. G. Shapiro and G. C. Stockman, Computer Vision (Prentice Hall, 2001).
25. J. M. Liu and Y. Y. Tang, "Adaptive image segmentation with distributed behavior-based agents," IEEE Trans. Pattern Anal. Mach. Intell. 21, 544-551 (1999).
26. J. M. Liu, H. Jing, and Y. Y. Tang, "Multi-agent oriented constraint satisfaction," Artif. Intell. 136, 101-144 (2002).
27. D. K. Panjwani and G. Healey, "Markov Random-Field Models For Unsupervised Segmentation Of Textured Color Images," IEEE Trans. Pattern Anal. Mach. Intell. 17, 939-954 (1995).
28. T. Ojala and M. Pietikainen, "Unsupervised texture segmentation using feature distributions," Pattern Recogn. 32, 477-486 (1999).
29. A. K. Jain and K. Karu, "Learning texture discrimination masks," IEEE Trans. Pattern Anal. Mach. Intell. 18, 195-205 (1996).
30. S. Lee and M. M. Crawford, "Unsupervised multistage image classification using hierarchical clustering with a Bayesian similarity measure," IEEE Trans. Image Process. 14, 312-320 (2005).
31. L. Itti, C. Koch, and E. Niebur, "A model of saliency-based visual attention for rapid scene analysis," IEEE Trans. Pattern Anal. Mach. Intell. 20, 1254-1259 (1998).
32. D. Walther, L. Itti, M. Riesenhuber, T. Poggio, and C. Koch, "Attentional Selection for Object Recognition - a Gentle Way," in Proceedings of Biologically Motivated Computer Vision, Second International Workshop (Tubingen, Germany, 2002), pp. 472-179.
33. A. Sha'ashua and S. Ullman, "Structural Saliency: The Detection of Globally Salient Struc-tures Using a Locally Connected Network," in Proceedings of The International Conference on Computer Vision (Tarpon Springs, Florida, 1988), pp. 321-327.
34. M. Meister and M. Berry, "The Neural Code of the Retina," Neuron 22, 435-450 (1999).
35. X. L. Wu, "Image-Coding By Adaptive Tree-Structured Segmentation," IEEE Trans. Inf. Theory 38, 1755-1767 (1992).
36. M. G. H. Omran, A. Salman, and A. P. Engelbrecht, "Dynamic clustering using particle swarm optimization with application in image segmentation," Pattern Anal. Appl. 8, 332-344 (2006).
37. C. Bourjot, V. Chevrier, and V. Thomas, "How Social Spiders Inspired an Approach to Region Detection," in Proceedings of International Conference on Autonomous Agents and MultiAgent Systems (Bologne, Italy, 2002), pp. 426-433.
38. Y. Wang and B. Yuan, "Fast method for face location and tracking by distributed behaviour-based agents," IEE Proc.-Vis. Image Signal Process. 149, 173-178 (2002).
39. T. Mirzayans, N. Parimi, P. Pilarski, C. Backhouse, L. Wyard-Scott, and P. Musilek, "A swarm-based system for object recognition," Neural Netw. World 15, 243-255 (2005).
40. A. Broggi, M. Cellario, P. Lombardi, and M. Porta, "An evolutionary approach to visual sensing for vehicle navigation," IEEE Trans. Ind. Electron. 50, 18-29 (2003).
41. A. P. Engelbrecht, Computational Intelligence: An Introduction (John Wiley & Sons, 2002).
42. B. Prasad, S. Du, W. Badawy, and K. Kaler, "A real-time multiple-cell tracking platform for dielectrophoresis (DEP)-based cellular analysis," Meas. Sci. Technol. 16, 909-924 (2005).
43. R. Ghrist and D. Lipsky, "Gramatical Self Assembly for Planar Tiles," in Proceedings of International Conference on MEMS, NANO and Smart Systems, W. Badawy and W. Moussa, eds. (Banff, Alberta, 2004), pp. 205-211.
44. R. O. Duda, P. E. Hart, and D. G. Stork, Pattern Classification (2nd Ed., Wiley Interscience, New York, 2001).
45. I. H. Witten and E. Frank, Data Mining: Practical Machine Learning Tools and Techniques (Morgan Kaufmann, 2005).
46. J. K. Udupa, V. R. LeBlanc, Z. G. Ying, C. Imielinska, H. Schmidt, L. M. Currie, B. E. Hirsch, and J. Wood-burn, "A framework for evaluating image segmentation algorithms," Comput. Med. Imaging Graph. 30(2), 75-87 (2006).
47. L. Bergman, A. Verikas, and M. Bacauskiene, "Unsupervised colour image segmentation applied to printing quality assessment," Image Vision Comput. 23, 417-425 (2005).
48. J.R. Taylor, An introduction to error analysis (2nd Ed., University Science Books, Sausalito, California, 1997).
49. V. Navalpakkam and L. Itti, "Modeling the influence of task on attention," Vision Res. 45, 205-231 (2005).
50. P.M. Pilarski, V.J. Sieben, C. Debes Marun, and C.J. Backhouse, "An artificial intelligence system for detecting abnormal chromosomes in malignant lymphocytes," in Proceedings of Canadian Society for Immunology, Annual Conference (Halifax, Canada, 2006), pp. 126.