SPOT DETECTION METHODS IN FLUORESCENCE MICROSCOPY IMAGING: A REVIEW

Authors

DOI:

https://doi.org/10.5566/ias.1690

Keywords:

fluorescence microscopy, microscopy image analysis, spot detection, supervised, unsupervised

Abstract

Fluorescence microscopy imaging has become one of the essential tools used by biologists to visualize and study intracellular particles within a cell. Studying these particles is a long-term research effort in the field of microscopy image analysis, consisting of discovering the relationship between the dynamics of particles and their functions. However, biologists are faced with challenges such as the counting and tracking of these intracellular particles. To overcome the issues faced by biologists, tools which can extract the location and motion of these particles are essential. One of the most important steps in these analyses is to accurately detect particle positions in an image, termed spot detection. The detection of spots in microscopy imaging is seen as a critical step for further quantitative analysis. However, the evaluation of these microscopic images is mainly conducted manually, with automated methods becoming popular. This work presents some advances in fluorescence microscopy image analysis, focusing on the detection methods needed for quantifying the location of these spots. We review several existing detection methods in microscopy imaging, along with existing synthetic benchmark datasets and evaluation metrics.

Author Biographies

  • Matsilele Aubrey Mabaso, CSIR
    Mobile Intelligent Autonomous Systems, Researcher
  • Daniel James Withey, CSIR

    Mobile Intelligent Autonomous Systems, Senior Researcher

  • Bhekisipho Twala, UNISA

    Department: Electrical and Mining Engineering, Director

References

Basset A, Boulanger J, Salamero J, Bouthemy P, Kervrann C (2015). Adaptive spot detection withn optimal scale selection in fluorescence microscopy images. IEEE Transactions on Image Processing 24(11): 4512-27.

Boulanger J, Kervrann C, Bouthemy P, Elbau P, Sibarita JB, Salamero J (2010). Patch-based nonlocal functional for denoising fluorescence microscopy image sequences. IEEE Transactions on Medical Imaging 29(2): 442-54.

Boykov Y, Kolmogorov V (2004). An experimental comparison of min-cut/max-flow algorithms for energy minimization in vision. IEEE Transactions on Pattern Analysis and Machine Intelligence 26(9):1124-37.

Byun J, Verardo MR, Sumengen B, Lewis GP, Manjunath BS, Fisher SK (2006). Automated tool for the detection of cell nuclei in digital microscopic images: Application to retinal images. Molecular Vision 12(1): 949-60.

Chenouard N (2016). Particle tracking benchmark generator. [Online]

Available at: http://icy.bioimageanalysis.org/plugin/Particle_tracking_benchmark_generator

[Accessed 1 September 2016].

Chenouard N, Smal I, de Chaumont F, Maška M, Sbalzarini IF, Gong Y, Cardinale J, Carthel C, Coraluppi S, Winter M, Cohen AR, Godinez WJ, Rohr K, Kalaidzidis Y, Liang L, Duncan J, Shen H, Xu Y, Magnusson KE, Jaldén J, Blau HM, Paul-Gilloteaux P, Roudot P, Kervrann C, Waharte F, Tinevez JY, Shorte SL, Willemse J, Celler K, van Wezel GP, Dan HW, Tsai YS, Ortiz de Solórzano C, Olivo-Marin J-C, Meijering E (2014). Objective comparison of particle tracking methods. Nature Methods 11(3):281-90.

Dabov K, Foi A, Katkovnik V, Egaizarian K (2007). Image denoising by sparse 3D transfrom-domain collaborative filtering. IEEE Transactions on Image Processing 16:1-16.

Fukunaga K, Hostetler L. D (1975). The estimation of the gradient of a density function, with applications in pattern recognition. IEEE Transactions on Information Theory 2(1)1:32-40.

Genovesio A, Liedl T, Emiliani V, Parak WJ, Coppey-Moisan M, Olivo-Marin JC (2006). Multiple particle tracking in 3d+t microscopy: Method and application to the tracking of endocytosed quantum dots. IEEE Transactions on Image Processing 15(5):1062-70.

Gerig G, Kubler O, Kikinis R, Jolesz FA (1992). Nonlinear anisotropic filtering of MRI data. IEEE Transactions on Medical Imaging, 11(2):221-32.

Gue M, Messaoudi C, Sun JS, Boudier T (2005). Smart 3d-fish: automation of distance analysis in nuclei of interphase cells by image processing. Cytometry Part A 67A(1):18-26.

Jackson C, Glory-Afshar E, Murphy RF, Kovačević J (2011). Model building and intelligent acquisition with application to protein subcellular location classification. Bioinformatics 27(13):1854-59.

Jaiswal A, Godinez WJ, Eils R, Lehmann MJ, Rohr K (2015). Tracking virus particles in fluorescence microscopy images using multi-scale detection and multi-frame association. IEEE Transactions on Image Processing 24(11):4122-36.

Jiajun D, Zhongtian C, Xiongxiong H, Yizhao Z (2016). Clustering by finding density peaks based on Chebyshev’s inequality. In: Proceedings of the 35th Chinese Control Confrerence 7169-72.

Jiang, S, Zhou X, Kirchhausen T, Wong SC (2007). Detection of molecular particles in live cells via machine learning. Cytometry Part A 71A(8):563-575.

Kervram C, Sorzano CÓS, Action ST, Olivo-Marin JC, Unser Michael (2016). A guided tour of selected image processing and analysis methods for fluorescence and electron microscopy. IEEE Journal of Selected Topics in Signal Processing 10(1):6-30.

Kimori Y, Baba, N, Morone N (2010). Extended morphological processing: a practical method for automatic spot detection of biological markers from microscopic images. BMC Bioinformatics 11(373):1-13.

Kleinberg E (1996). An overtraining-resistant stochastic modelling method for pattern recognition. Annals of Statistics 24(6):2319-49.

Kozubek M, Kozubek S, Lukásová E, Marecková A, Bártová E, Skalníková M, Jergová A (1999). High-resolution cytometry of fish dots in interphase cell nuclei. Cytometry 36(4):279-93.

LeCun Y, Bengio Y, Hinton G (2015). Deep learning. Nature 521(7553):436-44.

Lehmussola A, Ruusuvuori P, Selinummi J, Huttunen H, Yli-Harja O (2007). Computational framework for simulating fluorescence microscopy images with cell population. IEEE Transactions on Medical Imaging 26(7):1010-16.

Lindeberg T (1998). Feature detection with automatic scale selection. International Journal of Computer Vision 30(2):79-116.

Mabaso M, Withey D, Twala B (2016). A framework for creating realistic synthetic fluorescence microscopy image sequences. In: Proceedings of the 3rd International Conference on Bioimaging 85-92.

Marquardt DW (1963). An algorithm for least-squares estimation of nonlinear parameters. Journal of the Society for Industrial and Applied Mathematics 11(2):431-41.

Matula P, Verissimo F, Wörz S, Eils R, Pepperkok R, Rohr K (2010). Quantification of fluorescence spots in time series of 3-D confocal microscopy images of endoplasmic reticulum exit sites based on the HMAX transform. In: proc SPIE 7626:1-7

McLachlan GJ (2004). Discriminant analysis and statistical pattern recognition. John Wiley & Sons.

Netten H, Young IT, van Vliet LJ, Tanke HJ, Vroljik H, Sloos WC.Netten (1997). Fish and chips: automated of fluorescent dot counting in interphase cell nuclei. Cytometry 28(1):1-10.

Olivo-Marin J-C (2002). Extraction of spots in biological images using multiscale products. Pattern Recognition, 35(9):1989-96.

Otsu N (1979). A thresold selection method from gray-level histograms. IEEE Transactions on Systems, Man, and Cybernetics 9(1):62-66.

Pécot T, Bouthemy P, Boulanger J, Chessel A, Bardin S, Salamero J, Kervrann C (2015). Background fluorescence estimation and vesicle segmentation in live cell imaging with conditional random fields. IEEE Transactions on Image Processing 24(2):667-80.

Raj A (2016). Raj laboratory for system biology. [Online] Available at: http://rajlab.seas.upenn.edu

[Accessed 20 August 2016].

Raj A, van den Bogaard P, Rifkin SA, van Oudenaarden A, Tyagi S (2008). Imaging individual mRNA molecules using multiple singly labeled probes. Nature Methods 5(10):877-79.

Ram S, Rodriguez JJ, Bosco G (2012). Segmentation and detection of fluorescence 3d spots. Cytometry, 81A:198-212.

Rezatofighi SH, Gould S, Vo BT, Vo BN, Mele K, Hartley R (2015). Multi-target tracking with time-varying clutter rate and detection profile: application to time-lapse cell microscopy sequences. IEEE Transactions on Medical Imaging 34(6):1-14.

Rezatofighi SH, Hartley R, Hughes WE (2012). A new approach for spot detection in total internal reflection fluorescence microscopy. In: Proceedings of the 9th IEEE Int Symp on Biomedical Imaging (ISBI) 860-63.

Ruusuvuori P, Aijö T, Chowdhury S, Garmendia-Torres C, Selinummi J, Birbaumer M, Dudley AM, Pelkmans L, Yli-Harja O (2010). Evaluation of methods for detection of fluorescence labeled subcellular objects in microscope images. BMC Bioinformatics 11:1-17.

Ruusuvuori P, Lehmussola A, Selinummi J, Rajala T, Huttunen H, Yli-Harja O (2008). Benchmark set of synthetic images for validating cell image analysis algorithms. In: Proceedings of the 16th European Signal Processing Conference (EUSIPCO), 1-5

Ruusuvuori P, Manninen T, Huttunen H (2012). Image segmentation using sparse logistic regression with spatial prior. In: Proceedings of the 16th European Signal Processing Conference (EUSIPCO, 2253-57

Sage D, Neumann FR, Hediger F, Gasser SM, Unser M (2005). Automatic Tracking of Individual Fluorescence Particles: Application to the Study of Chromosome Dynamics. IEEE Transactions on Image Processing 14(9):1372-83.

Sbalzarini IF, Koumoutsakos P (2005). Feature Point Tracking and Trajectory Analysis for Video Imaging in Cell Biology. Journal of Structural Biology 151(2):182-95.

Smal I (2009). [Online] Available at: http://smal.ws/wp/software/synthetic-data-generator/

[Accessed 8 November 2016].

Smal I, Loog M, Niessen W, Meijering E (2010). Quantitative comparison of spot detection methods in fluorescence microscopy. IEEE Transactions on Medical Imaging 29(2):282-301.

Smal I, Meijering E, Draegestein K, Galjart N, Grigoriev I, Akhmanova A, van Royen ME, Houtsmuller AB, Niessen W (2008). Multiple object tracking in molecular bioimaging by Rao-Blackwellized marginal particle filtering. Medical Image Analysis 12(6):764-77.

Tanaka K, Inoue J-I, Titterington DM (2003). Probabilistic image processing by means of the Bethe approximation for the Q-Ising model. Journal of Physics, 36:1-15.

Tibshirani R (1994). Regression shrinkage and selection via the Lasso. Journal of the Royal Statistical Society, Series B, 58:267-88.

Velasco DF (1980). Thresholding using the ISODATA clustering algorithm. IEEE Transactions on Systems, Man, and Cybernetics 10:771-74.

Vincent L (1993). Morphological grayscale reconstruction in image analysis: Applications and efficient algorithms. IEEE Transactions on Image Processing 2:176-201.

Viola P, Jones M (2001). Rapid object detection using a boosted cascade of simple features. In: Proceedings of the IEEE Computer Society Conference on Computer Vision and Pattren Recognition (CVPR), 511-18.

Wilson RS, Yang L, Dun A, Smyth AM, Duncan RR, Rickman C, Lu W (2016). Automated single particle detection and tracking for large microscopy datasets. Royal Society Open Science 3(5):1-13.

Worz S, Sander P, Pfannmoller M, Rieker RJ, Joos S, Mechtersheimer G, Boukamp P, Lichter P, Rohr K (2010). 3D geometry-based quantification of colocalizations in multichannel 3d microscopy images of human soft tissue tumors. IEEE Transactions on Medical Imaging 29(8):1474-84.

Yang L, Parton R, Ball G, Qiu Z, Greenaway AH, Davis I, Lu W (2010). An adaptive non-local means filter for denoising live-cell images and improving particle detection. Journal of Structural Biology 172(3):233-43.

Downloads

Published

2018-12-06

Issue

Section

Review Article

How to Cite

Mabaso, M. A., Withey, D. J., & Twala, B. (2018). SPOT DETECTION METHODS IN FLUORESCENCE MICROSCOPY IMAGING: A REVIEW. Image Analysis and Stereology, 37(3), 173-190. https://doi.org/10.5566/ias.1690