Quantitative comparison of spot detection methods in fluorescence microscopy

IEEE Transactions on Medical Imaging

Volumn 29, Issue 2, 2010, Pages 282-301

  Smal, Ihor ^a   Loog, Marco ^a   Niessen, Wiro ^a,c   Meijering, Erik ^b  

^a ERASMUS MEDICAL CENTER   (Netherlands)

^b Delft University of Technology ^*  (Netherlands)

^c DELFT UNIVERSITY OF TECHNOLOGY   (Netherlands)

Author keywords

Fluorescence microscopy; Image filtering; Machine learning; Noise reduction; Object detection

Indexed keywords

BIOLOGICAL STUDIES; DETECTION METHODS; FLUORESCENCE MICROSCOPY IMAGES; GROUND TRUTH; IMAGE DATA; LIVE-CELL IMAGING; MACHINE-LEARNING; MANUAL ANNOTATION; MULTISCALES; NOISE REDUCTIONS; OBJECT DETECTION; QUANTITATIVE ANALYSIS; QUANTITATIVE COMPARISON; QUANTITATIVE EVALUATION; REAL IMAGE DATA; SPOT DETECTION; SUBRESOLUTION; SYNTHETIC IMAGES; UNSUPERVISED METHOD;

ACOUSTIC NOISE MEASUREMENT; EXPERIMENTS; FLUORESCENCE; FLUORESCENCE MICROSCOPY; LEARNING SYSTEMS; MATHEMATICAL MORPHOLOGY; MATHEMATICAL TRANSFORMATIONS; SIGNAL TO NOISE RATIO;

DETECTORS;

GREEN FLUORESCENT PROTEIN;

ALGORITHM; ARTICLE; ARTIFICIAL INTELLIGENCE; COMPARATIVE STUDY; COMPUTER SIMULATION; CYTOLOGY; DISCRIMINANT ANALYSIS; EUKARYOTIC CELL; FLUORESCENCE MICROSCOPY; HELA CELL; HUMAN; IMAGE PROCESSING; METHODOLOGY; MICROTUBULE; NORMAL DISTRIBUTION; POISSON DISTRIBUTION; RECEIVER OPERATING CHARACTERISTIC; SENSITIVITY AND SPECIFICITY; THEORETICAL MODEL;

ALGORITHMS; ARTIFICIAL INTELLIGENCE; COMPUTER SIMULATION; DISCRIMINANT ANALYSIS; EUKARYOTIC CELLS; GREEN FLUORESCENT PROTEINS; HELA CELLS; HUMANS; IMAGE PROCESSING, COMPUTER-ASSISTED; MICROSCOPY, FLUORESCENCE; MICROTUBULES; MODELS, THEORETICAL; NORMAL DISTRIBUTION; POISSON DISTRIBUTION; ROC CURVE; SENSITIVITY AND SPECIFICITY;

EID: 76249126657     PISSN: 02780062     EISSN: None     Source Type: Journal    
DOI: 10.1109/TMI.2009.2025127     Document Type: Article

References (81)

1. J. Lippincott-Schwartz and G. H. Patterson, "Development and use of fluorescent protein markers in living cells, " Science, vol.300, no.5616, pp. 87-91, 2003.
2. A. Miyawaki, A. Sawano, and T. Kogure, "Lighting up cells: Labelling proteins with fluorophores, " Nature Cell Biol., vol.5, pp. S1-S7, 2003.
3. D. J. Stephens and V. J. Allan, "Light microscopy techniques for live cell imaging, " Science, vol.300, no.5616, pp. 82-86, 2003.
4. R. Y. Tsien, "Imagining imaging's future, " Nature Cell Biol., vol.5, pp. S16-S21, 2003.
5. R. Pepperkok and J. Ellenberg, "High-throughput fluorescence microscopy for systems biology, " Nature Rev. Molecular Cell Biol., vol.7, no.9, pp. 690-696, 2006.
6. C. Vonesch, F. Aguet, J.-L. Vonesch, and M. Unser, "The colored revolution of bioimaging, " IEEE Signal Process. Mag., vol.23, no.3, pp. 20-31, May 2006.
7. R. Eils and C. Athale, "Computational imaging in cell biology, " J. Cell Biol., vol.161, no.3, pp. 477-481, 2003.
8. X. Zhou and S. T. C. Wong, "Informatics challenges of high-throughput microscopy, " IEEE Signal Process. Mag., vol.23, no.3, pp. 63-72, May 2006.
9. E. Meijering and G. van Cappellen, "Quantitative biological image analysis, " in Imaging Cellular and Molecular Biological Functions, S. L. Shorte and F. Frischknecht, Eds. Berlin, Germany: Springer-Verlag, 2007, pp. 45-70.
10. W. M. Ahmed, S. J. Leavesley, B. Rajwa, M. N. Ayyaz, A. Ghafoor, and J. P. Robinson, "State of the art in information extraction and quantitative analysis for multimodality biomolecular imaging, " Proc. IEEE, vol.96, no.3, pp. 512-531, Mar. 2008.
11. Q. Wu, F. A. Merchant, and K. R. Castleman, Microscope Image Processing. Burlington, MA: Elsevier, 2008.
12. D. A. Drubin, A. M. Garakani, and P. A. Silver, "Motion as a phe-notype: The use of live-cell imaging and machine visual screening to characterize transcription-dependent chromosome dynamics, " BMC Cell Biol., vol.7, p. 19, 2006.
13. B. Neumann, M. Held, U. Liebel, H. Erfle, P. Rogers, R. Pepperkok, and J. Ellenberg, "High-throughput RNAi screening by time-lapse imaging of live human cells, " Nature Meth., vol.3, no.5, pp. 385-390, 2006.
14. J. F. Dorn, G. Danuser, and Y. Ge, "Computational processing and analysis of dynamic fluorescence image data, " Meth. Cell Biol., vol.25, pp. 497-538, 2008.
15. D. Gerlich and J. Ellenberg, "4D imaging to assay complex dynamics in live specimens, " Nature Cell Biol., vol.5, pp. S14-S19, 2003.
16. E. Meijering, I. Smal, and G. Danuser, "Tracking in molecular bioimaging, " IEEE Signal Process. Mag., vol.23, no.3, pp. 46-53, May 2006.
17. E. Meijering, I. Smal, O. Dzyubachyk, and J.-C. Olivo-Marin, "Time-lapse imaging, " in Microscope Image Processing, Q. Wu, F. A. Merchant, and K. R. Castleman, Eds. Burlington, MA: Elsevier, 2008, pp. 401-440.
18. S. W. Hell, M. Dyba, and S. Jakobs, "Concepts for nanoscale resolution in fluorescence microscopy, " Current Opinion Neurobiol., vol.14, no.5, pp. 599-609, 2004.
19. Y. Garini, B. J. Vermolen, and I. T. Young, "From micro to nano: Recent advances in high-resolution microscopy, " Current Opinion Biotechnol., vol.16, no.1, pp. 3-12, 2005.
20. I. Smal, K. Draegestein, N. Galjart, W. Niessen, andE. Meijering, "Particle filtering for multiple object tracking in dynamic fluorescence microscopy images: Application to microtubule growth analysis, " IEEE Trans. Med. Imag., vol.27, no.6, pp. 789-804, Jun. 2008.
21. I. Smal, E. Meijering, K. Draegestein, N. Galjart, I. Grigoriev, A. Akhmanova, M. E. van Royen, A. B. Houtsmuller, and W. Niessen, "Multiple object tracking in molecular bioimaging by rao-black-wellized marginal particle filtering, " Med. Image Anal., vol.12, no.6, pp. 764-777, 2008.
22. I. Smal, W. Niessen, and E. Meijering, "A new detection scheme for multiple object tracking in fluorescence microscopy by joint probabilistic data association filtering, " in Proc. IEEE Int. Symp. Biomed. Imag., 2008, pp. 264-267.
23. M. K. Cheezum, W. F. Walker, and W. H. Guilford, "Quantitative comparison of algorithms for tracking single fluorescent particles, " Bio-phys. J., vol.81, no.4, pp. 2378-2388, 2001.
24. B. C. Carter, G. T. Shubeita, and S. P. Gross, "Tracking single particles: A user-friendly quantitative evaluation, " Phys. Biol., vol.2, no.1, pp. 60-72, 2005.
25. D. S. Bright and E. B. Steel, "Two-dimensional top hat filter for extracting spots and spheres from digital images, " J. Microsc., vol.146, no.2, pp. 191-200, 1987.
26. E. J. Breen, G. H. Joss, and K. L. Williams, "Locating objects ofinterest within biological images: The top hat box filter, " J. Comput.-Assist. Microsc., vol.3, no.2, pp. 97-102, 1991.
27. D. Thomann, D. R. Rines, P. K. Sorger, and G. Danuser, "Automatic fluorescent tag detection in 3D with super-resolution: Application to the analysis of chromosome movement, " J. Microsc., vol.208, no.1, pp. 49-64, 2002.
28. D. Sage, F. R. Neumann, F. Hediger, S. M. Gasser, and M. Unser, "Automatic tracking of individual fluorescence particles: Applicationto the study of chromosome dynamics, " IEEE Trans. Image Process., vol.14, no.9, pp. 1372-1383, Sep. 2005.
29. B. Zhang, J. Fadili, J.-L. Starck, and J.-C. Olivo-Marin, "Multiscale variance-stabilizing transform for mixed-Poisson-Gaussian processes and its applications in bioimaging, " in Proc. IEEE Int. Conf. Image Process., Oct. 2007, vol.6, pp. VI-233-VI-236.
30. A. Genovesio, T. Liedl, V. Emiliani, W. J. Parak, M. Coppey-Moisan, and J.-C. Olivo-Marin, "Multiple particle tracking in 3-D+t microscopy: Method and application to the tracking of endocytosed quantum dots, " IEEE Trans. Image Process., vol.15, no.5, pp. 1062-1070, May 2006.
31. J.-C. Olivo-Marin, "Extraction of spots in biological images using mul-tiscale products, " Pattern Recognit., vol.35, no.9, pp. 1989-1996, 2002.
32. S. Jiang, X. Zhou, T. Kirchhausen, and S. T. C. Wong, "Detection of molecular particles in live cells via machine learning, " Cytometry A, vol.71, no.8, pp. 563-575, 2007. (Pubitemid 47221718)
33. B. Zhang, J. Zerubia, and J.-C. Olivo-Marin, "Gaussian approximations of fluorescence microscope point-spread function models, " Appl. Opt., vol.46, no.10, pp. 1819-1829, 2007.
34. J. Lippincott-Schwartz, N. Altan-Bonnet, and G. H. Patterson, "Pho-tobleaching and photoactivation: Following protein dynamics in living cells, " Nature Cell Biol., vol.5, pp. S7-S13, 2003.
35. B. M. ter Haar Romeny, Front-End Vision and Multi-Scale Image Analysis. Berlin, Germany: Springer, 2003.
36. G. L. Turin, "An introduction to matched filters, " IRE Trans. Inform. Theory, vol.6, no.3, pp. 311-329, 1960.
37. L. Vincent, "Morphological grayscale reconstruction inimage analysis: Applications and efficient algorithms, " IEEE Trans. Image Process., vol.2, no.1, pp. 176-201, Feb. 1993.
38. J. Serra, Image Analysis and Mathematical Morphology. London, U. K.: Academic, 1982.
39. M. Sonka, V. Hlavac, and R. Boyle, Image Processing, Analysis, and Machine Vision, 2nd ed. Pacific Grove, CA: PWS, 1999.
40. J.-L. Starck, J. Fadili, and F. Murtagh, "The undecimated wavelet decomposition and its reconstruction, " IEEE Trans. Image Process., vol.16, no.2, pp. 297-309, Feb. 2007.
41. J.-L. Starck and F. Murtagh, Astronomical Image and Data Analysis. Berlin, Germany: Springer-Verlag, 2002.
42. S. Mallat, A Wavelet Tour of Signal Processing. New York: Academic, 1998.
43. J.-L. Starck, M. Elad, and D. Donoho, "Redundant multiscale transforms and their application for morphological component analysis, " in Advances in Imaging and Electron Physics. San Diego, CA: Elsevier, 2004, vol.132, pp. 288-348.
44. J.-L. Starck and F. Murtagh, "Astronomical image and signal processing: Looking at noise, information and scale, " IEEE Signal Process. Mag., vol.18, no.2, pp. 30-40, Feb. 2001.
45. M. A. T. Figueiredo and R. D. Nowak, "Wavelet-based image estimation: An empirical bayes approach using Jeffrey's noninformative prior, " IEEE Trans. Image Process., vol.10, no.9, pp. 1322-1331, Sep. 2001.
46. Y. Fong, C. Pomalaza, and X. Wang, "Comparison study of nonlinear filters in image processing applications, " Opt. Eng., vol.28, pp. 749-760, 1989.
47. C. Tomasi and R. Manduchi, "Bilateral filtering for gray and color images, " in Proc. 6th Int. Conf. Comput. Vis., 1998, pp. 839-846.
48. D. Comaniciu and P. Meer, "Mean shift: A robust approach toward feature space analysis, " IEEE Trans. Pattern Anal. Mach. Intell., vol.24, no.5, pp. 603-619, May 2002.
49. P. Perona and J. Malik, "Scale-space and edge detection using anisotropic diffusion, " IEEE Trans. Pattern Anal. Mach. Intell., vol.12, no.7, pp. 629-639, Jul. 1990.
50. J. Boulanger, C. Kervrann, and P. Bouthemy, "Space-time adaptation for patch-based image sequence restoration, " IEEE Trans. Pattern Anal. Mach. Intell., vol.29, no.6, pp. 1096-1102, Jun. 2007.
51. J. Boulanger, J.-B. Sibarita, C. Kervrann, andP. Bouthemy, "Non-parametric regression for patch-based fluorescence microscopy image sequence denoising, " in Proc. IEEE Int. Symp. Biomed. Imag., 2008, pp. 748-751.
52. J. Boulanger, C. Kervrann, and P. Bouthemy, "A simulation and estimation framework for intracellular dynamics and trafficking in video-microscopy and fluorescence imagery, " Med. Image Anal., vol.13, no.1, pp. 132-142, 2008.
53. L. J. van Vliet, D. Sudar, and I. T. Young, "Digitalfluorescence imaging using cooled charge-coupled device array cameras, " in Cell Biology: A Laboratory Handbook, K. Simons, Ed., 2nd ed. New York: Academic, 1998, pp. 109-120.
54. F. J. Anscombe, "The transformation of Poisson, binomial and negative-binomial data, " Biometrika, vol. 35, no. 3/4, pp. 246-254, 1948.
55. J.-L. Starck, F. Murtagh, and A. Bijaoui, Image Processing and Data Analysis, the Multiscale Approach. Cambridge, U. K.: Cambridge Univ. Press, 2000.
56. B. J. Vermolen, Y. Garini, I. T. Young, R. W. Dirks, and V. Raz, "Segmentation and analysis of the three-dimensional redistribution of nuclear components in human mesenchymal stem cells, " Cytometry A, vol.73, no.9, pp. 816-824, 2008.
57. P. Soille, Morphological Image Analysis: Principles and Applications. Berlin, Germany: Springer-Verlag, 2003.
58. S. M. Arulampalam, S. Maskell, N. Gordon, and T. Clapp, "A tutorial on particle filters for online nonlinear/non-Gaussian Bayesian tracking, " IEEE Trans. Signal Process., vol.50, no.2, pp. 174-188, Feb. 2002.
59. T. Lindeberg, "Feature detection with automatic scale selection, " Int. J. Comput. Vis., vol.30, no.2, pp. 79-116, 1998. (Pubitemid 128517917)
60. P. Viola and M. Jones, "Rapid object detection using a boosted cascade of simple features, " in Proc. 2001 IEEE Comput. Soc. Conf. Comput. Vis. Pattern Recognit., 2001, vol.1, pp. I-511-I-518.
61. Y. Freund and R. E. Schapire, "Experiments with a new boosting algorithm, " in Proc. 13th Int. Conf. Mach. Learn., 1996, pp. 148-156.
62. R. Lienhart and J. Maydt, "An extended set of Haar-like features for rapid object detection, " in Proc. 2002 Int. Conf. Image Process., 2002, vol.1, pp. I-900.
63. C. Papageorgiou, M. Oren, and T. Poggio, "A general framework for object detection, " in Proc. 6th Int. Conf. Comput. Vis., 1998, pp. 555-562.
64. G. J. McLachlan, Discriminant Analysis and Statistical Pattern Recognition. New York: Wiley, 2004.
65. B. K. Horn, Robot Vision. Cambridge, MA: MIT Press, 1986.
66. D. P. Chakraborty, "Maximum likelihood analysis of free-response receiver operating characteristic (FROC) data, " Med. Phys., vol.16, no.4, pp. 561-568, 1989.
67. D. P. Chakraborty, "Free-response methodology: Alternate analysis and a new observer-performance experiment, " Radiol., vol.174, pp. 873-881, 1990.
68. C. P. Bacher, M. Reichenzeller, C. Athale, H. Herrmann, and R. Eils, "4-D single particle tracking of synthetic and proteinaceous micro-spheres reveals preferential movement of nuclear particles along chro-matin-poor tracks, " BMC Cell Biol., vol.5, no.45, pp. 1-14, 2004.
69. I. F. Sbalzarini and P. Koumoutsakos, "Feature point tracking and trajectory analysis for video imaging in cell biology, " J. Struct. Biol., vol.151, no.2, pp. 182-195, 2005.
70. S. Bonneau, M. Dahan, and L. D. Cohen, "Single quantum dot tracking based on perceptual grouping using minimal paths in a spatiotemporal volume, " IEEE Trans. Image Process., vol.14, no.9, pp. 1384-1395, Sep. 2005.
71. Y. Kalaidzidis, "Intracellular objects tracking, " Eur. J. Cell Biol., vol.86, no.9, pp. 569-578, Sep. 2007.
72. S. Jiang, X. Zhou, T. Kirchhausen, and S. T. C. Wong, "Tracking molecular particles in live cells using fuzzy rule-based system, " Cytometry A, vol.71, no.8, pp. 576-584, 2007.
73. K. Jaqaman, D. Loerke, M. Mettlen, H. Kuwata, S. Grinstein, S. L. Schmid, and G. Danuser, "Robust single-particle tracking in live-cell time-lapse sequences, " Nature Meth., vol.5, no.8, pp. 695-702, 2008.
74. G. Lansbergen and A. Akhmanova, "Microtubule plus end: A hub of cellular activities, " Traffic, vol.7, no.5, pp. 499-507, 2006.
75. T. Stepanova, J. Slemmer, C. C. Hoogenraad, G. Lansbergen, B. Dort-land, C. I. de Zeeuw, F. Grosveld, G. van Cappellen, A. Akhmanova, and N. Galjart, "Visualization of microtubule growth in cultured neurons via the use of EB3-GFP (end-binding protein 3-green fluorescent protein), " J. Neurosci., vol.23, no.7, pp. 2655-2664, 2003.
76. I. Grigoriev, D. Splinter, N. Keijzer, P. S. Wulf, J. Demmers, T. Oht-suka, M. Modesti, I. V. Maly, F. Grosveld, C. C. Hoogenraad, and A. Akhmanova, "Rab6 regulates transport and targeting of exocytotic carriers, " Develop. Cell, vol.13, no.2, pp. 305-314, 2007.
77. E. Meijering, MTrackJ: A Java Program for Manual Object Tracking 2006
78. [Online]. Available: http://www. imagescience. org/mei-jering/software/ mtrackj/
79. W. S. Rasband, ImageJ: Image Processing and Analysis in Java
80. [Online]. Available: http://rsb. info. nih. gov/ij/
81. M. D. Abramoff, P. J. Magalhaes, and S. J. Ram, "Image processing with ImageJ, " Biophoton. Int., vol.11, no.7, pp. 36-42, 2004.