荧光显微图像亚细胞斑点检测方法研究进展

doi:10.3969/j.issn.0258-8021.2012.06.018

摘要
图/表
参考文献
相关文章 (2)

全文: PDF (580 KB) HTML (1 KB)
输出: BibTeX | EndNote (RIS)

摘要高通量荧光显微图像数据定量分析,是研究活细胞动态过程的有力工具。亚细胞观察对象在图像中常以衍射极限点斑的形式出现。成像条件的限制往往导致荧光显微图像均一度差、信噪比较低，给人工分析带来了挑战。设计自动化的亚细胞斑点检测方法，是高通量荧光显微图像数据处理的必要前提。本文就近年来斑点检测的关键技术进行了较详尽的综述，包括降噪滤波、信号增强和信号阈值化。在总结现有斑点检测方法优缺点的基础上，讨论算法设计的瓶颈和共性难点，并对相关研究做了展望。

	服务

	把本文推荐给朋友
	加入我的书架
	加入引用管理器
	E-mail Alert
	RSS
	作者相关文章
	吴坚¹赵挺²谭映军³李莹辉^3郑筱祥¹
	2*

关键词 ：斑点检测, 图像滤波, 点扩散函数, 荧光显微图像

Abstract：Quantitative analysis of high throughput fluorescence microscopic image is a powerful tool to study dynamic processes in living cells. Many subcellular objects of interest appear as diffraction-limited spots in the image. The limitations of imaging conditions often lead to fluorescence microscopic images inhomogeneous and lower signal-to-noise ratio (SNR), making manual analysis a very challenging task. Designing the automatic subcellular spot detection method is a prerequisite for high throughput fluorescence microscopic image processing. This review presented a detailed overview of recent advances in the key techniques of　spot detection method, including noise reduction, signal enhancement and signal thresholding. The bottlenecks and common difficulties of the algorithm design were discussed on the basis of summarizing advantages and disadvantages of the exiting spot detection methods. At the same time, the prospect of the related research was discussed.

Key words： spot detection image filtering point spread function fluorescence microscopic image

基金资助:航天医学基础与应用国家重点实验室开放基金

引用本文:

吴坚¹赵挺²谭映军³李莹辉^3郑筱祥^1,2*. 荧光显微图像亚细胞斑点检测方法研究进展[J]. 中国生物医学工程学报, 2012, 31(6): 925-933.
WU Jian^1ZHAO Ting^2TAN Ying Jun³LI Ying Hui³ZHENG Xiao Xiang^{1,2*
. Subcellular Spot Detection for Fluorescence Microscopic Images. journal1, 2012, 31(6): 925-933.}

链接本文:

http://cjbme.csbme.org/CN/10.3969/j.issn.0258-8021.2012.06.018 或 http://cjbme.csbme.org/CN/Y2012/V31/I6/925

［1］LippincottSchwartz J, Patterson GH. Development and use of fluorescent protein markers in living cells ［J］. Science, 2003, 300(5616): 87-91.
［2］Miyawaki A, Sawano A, Kogure T. Lighting up cells: Labelling proteins with fluorophores ［J］. Nature Cell Biology, 2003, 5(Suppl): 1-7.
［3］Stephens DJ, Allan VJ. Light microscopy techniques for live cell imaging ［J］. Science, 2003, 300(5616): 82-86.
［4］Tsien RY. Imagining imaging’s future ［J］. Nature Cell Biology, 2003, 5(Suppl): 16-21.
［5］Pepperkok R, Ellenberg J. High throughput fluorescence microscopy for systems biology ［J］. Nature Reviews Molecular Cell Biology, 2006, 7(9): 690-696.
［6］Vonesch C, Aguet F, Vonesch JL, et al. The colored revolution of bioimaging ［J］. IEEE Signal Processing Magazine, 2006, 23(3): 20-31.
［7］Eils R, Athale C. Computational imaging in cell biology ［J］. The Journal of Cell Biology, 2003, 161(3): 477-481.
［8］Zhou X, Wong STC. Informatics challenges of high throughput microscopy ［J］. IEEE Signal Processing Magazine, 2006, 23(3): 63-72.
［9］Shorte SL, Frischknecht F. Imaging Cellular and Molecular Biological Functions ［M］. Berlin: SpringerVerlag, 2007: 45-70.
［10］Ahmed WM, Leavesley SJ, Rajwa B, et al. State of the art in information extraction and quantitative analysis for multimodality biomolecular imaging ［J］. Proceedings of the IEEE, 2008, 96(3): 512-531.
［11］Wu Q, Merchant FA, Castleman KR. Microscope Image Processing ［M］. Burlington: Elsevier, 2008: 247-297.
［12］Drubin DA, Garakani AM, Silver PA. Motion as a phenotype: The use of livecell imaging and machine visual screening to characterize transcription dependent chromosome dynamics ［J/OL］. BMC Cell Biology, 2006, 7(19). http://www.biomedcentral.com/14712121/7/19, 20060424/20120718.
［13］Neumann B, Held M, Liebel U, et al. High throughput RNAi screening by timelapse imaging of live human cells ［J］. Nature Methods, 2006, 3(5): 385-390.
［14］Sacher R, Stergiou L, Pelkmans L. Lessons from genetics: interpreting complex phenotypes in RNAi screens ［J］. Current Opinion Cell Biology, 2008, 20(4): 483-489.
［15］Khodade P, Malhotra S, Kumar N, et al. Cytoview: development of a cell modelling framework ［J］. Journal of Bioscience, 2007, 32(5): 965-977.
［16］Jaqaman K, Loerke D, Mettlen M, et al. Robust singleparticle tracking in livecell timelapse sequences ［J］. Nature Methods, 2008, 5(8): 695-702.
［17］Sbalzarini IF, Koumoutsakos P. Feature point tracking and trajectory analysis for video imaging in cell biology ［J］. Journal of Structure Biology, 2005, 151(2):182-195.
［18］Smal I, Meijering E, Draegestein K, et al. Multiple object tracking in molecular bioimaging by raoblackwellized marginal particle filtering ［J］. Medical Image Analysis, 2008, 12(6): 764-777.
［19］Dorn JF, Danuser G, Ge Y. Computational processing and analysis of dynamic fluorescence image data ［J］. Methods in Cell Biology, 2008, 25: 497-538.
［20］Gerlich D, Ellenberg J. 4D imaging to assay complex dynamics in live specimens ［J］. Nature Cell Biology, 2003, 5(Suppl):14-19.
［21］Meijering E, Smal I, Danuser G. Tracking in molecular bioimaging ［J］. IEEE Signal Processing Magazine, 2006, 23(3): 46-53.
［22］Stevens JK, Mills LR, Trogadis JE. Three Dimensional Confocal Microscopy: Volume Investigation of Biological Specimens ［M］. London: Academic Press, 1994:47-93.
［23］Zhang B, Zerubia J, OlivoMarin JC. Gaussian approximations of fluorescence microscope point spread function models ［J］. Applied Optics, 2007, 46(10): 1819-1829.
［24］Thomann D, Rines DR, Sorger PK, et al. Automatic fluorescent tag detection in 3D with superresolution: Application to the analysis of chromosome movement ［J］. Journal of Microscopy, 2002, 208(1): 49-64.
［25］Romeny THBM. FrontEnd Vision and MultiScale Image Analysis ［M］. Berlin: Springer, 2003: 277-284.
［26］Turin GL. An introduction to matched filters ［J］. IRE Transactions on Information Theory, 1960, 6(3): 311-329.
［27］OlivoMarin JC. Extraction of spots in biological images using multiscale products ［J］. Pattern Recognition, 2002, 35(9): 1989-1996.
［28］Boulanger J, Kervrann C, Bouthemy P. Patchbased nonlocal functional for denoising fluorescence microscopy image sequences ［J］. IEEE Transactions on Medical Imaging, 2010, 29(2): 442-454.
［29］Zhang B, Fadili J, Starck JL. Multiscale variancestabilizing transform for mixedPoissonGaussian processes and its applications in bioimaging ［C］. Proceedings of IEEE International Conference on Image Processing. San Antonio: IEEE, 2007, 6(Ⅵ): 233-236.
［30］Kimori Y, Baba N, Morone N. Extended morphological processing: a practical method for automatic spot detection of biological markers from microscopic images ［J/OL］. BMC Bioinformatics, 2010, 11(373). http://www.biomedcentral.com/14712105/11/373,20100708/20120718.
［31］Rohr K, Godinez WJ, Harder N, et al. Tracking and quantitative analysis of dynamic movements of cells and particles ［J/OL］. Cold Spring Harb Protocols, 2010, 6: 116. http://cshprotocols.cshlp.org/content/2010/6/pdb.top80.full, 20100601/20120718.
［32］Sage D, Neumann FR, Hediger F, et al. Automatic tracking of individual fluorescence particles: Application to the study of chromosome dynamics ［J］. IEEE Transactions on Image Processing, 2005, 14(9): 1372-1383.
［33］Crocker JC, Grier DG. Methods of digital video microscopy for colloidal studies ［J］. Journal of colloid and interface science, 1996, 179: 298-310.
［34］Soille P. Morphological Image Analysis: Principles and Applications ［M］. Berlin: Springer Verlag, 2003: 121-127.
［35］Zhang B, Fadili J, Starck J. Wavelets, ridgelets, and curvelets for Poisson noise removal ［J］. IEEE Transactions on Image Processing, 2008, 17(7): 93-108.
［36］Ruusuvuori P, ij T, Chowdhury S, et al. Evaluation of methods for detection of fluorescence labeled subcellular objects in microscope images ［J/OL］. BMC Bioinformatics, 2010, 11(248). http://www.biomedcentral.com/14712105/11/248, 20100513/20120718. 
［37］Jiang S, Zhou X, Kirchhausen T, et al. Detection of molecular particles in live cells via machine learning ［J］. Cytometry A, 2007, 71(8): 563-575.
［38］Mclachlan GJ. Discriminant Analysis and Statistical Pattern Recognition ［M］. New York: Wiley, 2004: 129-336.
［39］Fawcett T. An introduction to ROC analysis ［J］. Pattern Recognition Letters, 2006, 27:861-874.
［40］Popovic A, De La Fuente M, Engelhardt M, et al. Statistical validation metric for accuracy assessment in medical image segmentation ［J］. International Journal of Computer Assisted Radiology and Surgery, 2007, 2:169-181.
［41］Swets JA. Measuring the accuracy of diagnostic systems ［J］. Science, 1988, 240:1285-1293. 
［42］Smal I, Loog M, Niessen W, et al. Quantitative comparison of spot detection methods in fluorescence microscopy ［J］. IEEE Transactions on Medical Imaging, 2010, 29(2): 282-301.
［43］Hell SW, Dyba M, Jakobs S. Concepts for nanoscale resolution in fluorescence microscopy ［J］. Current Opinion in Neurobiology, 2004, 14(5): 599-609.
［44］Garini Y, Vermolen BJ, Young IT. From micro to nano: Recent advances in highresolution microscopy ［J］. Current Opinion in Neurobiology, 2005, 16(1): 3-12. 
［45］Ruusuvuori P, Lehmussola A, Selinummi J, et al. Benchmark set of synthetic images for validating cell image analysis algorithms ［C］//Proceedings of the 16th European Signal Processing Conference. Lausanne: EUSIPCO, 2008: 1569105508.
［46］Broad bioimage benchmark collection ［DB/OL］. http://www.broad.mit.edu/bbbc, 2012-0521/20120718.
［47］Gelasca ED, Byun J, Obara B, et al. Evaluation and benchmark for biological image segmentation ［C］//Proceedings of IEEE International Conference on Image Processing. San Diego: IEEE, 2008: 1816-1819.