Light Microscopy pp 185-207 | Cite as
Designing Image Analysis Pipelines in Light Microscopy: A Rational Approach
- 5 Citations
- 2.8k Downloads
Abstract
With the progress of microscopy techniques and the rapidly growing amounts of acquired imaging data, there is an increased need for automated image processing and analysis solutions in biological studies. Each new application requires the design of a specific image analysis pipeline, by assembling a series of image processing operations. Many commercial or free bioimage analysis software are now available and several textbooks and reviews have presented the mathematical and computational fundamentals of image processing and analysis. Tens, if not hundreds, of algorithms and methods have been developed and integrated into image analysis software, resulting in a combinatorial explosion of possible image processing sequences. This paper presents a general guideline methodology to rationally address the design of image processing and analysis pipelines. The originality of the proposed approach is to follow an iterative, backwards procedure from the target objectives of analysis. The proposed goal-oriented strategy should help biologists to better apprehend image analysis in the context of their research and should allow them to efficiently interact with image processing specialists.
Key words
Light microscopy Image analysis Image processing Image segmentation Watershed transformReferences
- 1.Schindelin J, Arganda-Carreras I, Frise E et al (2012) Fiji: an open-source platform for biological-image analysis. Nat Methods 9:676–682CrossRefPubMedGoogle Scholar
- 2.Howard CV, Reed MG (1988) Unbiased stereology: three-dimensional measurement in microscopy. BIOS Scientific Publishers, OxfordGoogle Scholar
- 3.Pirard E, Dislaire G (2005) Robustness of planar shape descriptors of particles. Proc. Int. Assoc. Math. Geol. Conf. Toronto, CAGoogle Scholar
- 4.Lehmann G, Legland D (2012) Efficient N-dimensional surface estimation using Crofton formula and run-length encoding. Insight J. http://hdl.handle.net/10380/3342
- 5.Dorst L, Smeulders AWM (1987) Length estimators for digitized contours. Comput Vis Graph Image Process 40:311–333. doi: http://dx.doi.org/10.1016/S0734-189X(87)80145-7
- 6.Legland D, Arganda-Carreras I, Andrey P (2016) MorphoLibJ: mathematical morphology library for ImageJ. Release 1(2):2. doi: 10.5281/zenodo.51734 Google Scholar
- 7.Pincus Z, Theriot JA (2007) Comparison of quantitative methods for cell-shape analysis. J Microsc 227:140–156. doi: 10.1111/j.1365-2818.2007.01799.x CrossRefPubMedGoogle Scholar
- 8.Waters JC (2009) Accuracy and precision in quantitative fluorescence microscopy. J Cell Biol 185:1135–1148. doi: 10.1083/jcb.200903097 CrossRefPubMedPubMedCentralGoogle Scholar
- 9.Ronneberger O, Baddeley D, Scheipl F et al (2008) Spatial quantitative analysis of fluorescently labeled nuclear structures: problems, methods, pitfalls. Chromosom Res 16:523–562CrossRefGoogle Scholar
- 10.Bolte S, Cordelieres FP (2006) A guided tour into subcellular colocalization analysis in light microscopy. J Microsc 224:213–232CrossRefPubMedGoogle Scholar
- 11.Dunn KW, Kamocka MM, McDonald JH (2011) A practical guide to evaluating colocalization in biological microscopy. Am J Physiol Physiol 300:C723–C742CrossRefGoogle Scholar
- 12.Diggle PJ (2014) Statistical analysis of spatial and spatio-temporal point patterns, 3rd edn. Chapman and Hall/CRC Press, Boca RatonGoogle Scholar
- 13.Andrey P, Kiêu K, Kress C et al (2010) Statistical analysis of 3D images detects regular spatial distributions of centromeres and chromocenters in animal and plant nuclei. PLoS Comput Biol 6:e1000853CrossRefPubMedPubMedCentralGoogle Scholar
- 14.Meijering E, Smal I, Danuser G (2006) Tracking in molecular bioimaging. IEEE Signal Process Mag 23:46–53. doi: 10.1109/MSP.2006.1628877 CrossRefGoogle Scholar
- 15.Otsu N (1979) A threshold selection method from gray-level histograms. IEEE Trans Syst Man Cybern 9:62–66CrossRefGoogle Scholar
- 16.Kapur JN, Sahoo PK, Wong AKC (1985) A new method for gray-level picture thresholding using the entropy of the histogram. Comput Vis Graph Image Process 29:273–285CrossRefGoogle Scholar
- 17.Kittler J, Illingworth J (1986) Minimum error thresholding. Pattern Recognit 19:41–47CrossRefGoogle Scholar
- 18.Vincent L, Soille P (1991) Watersheds in digital spaces: an efficient algorithm based on immersion simulation. IEEE Trans Pattern Anal Mach Intell 13:583–598CrossRefGoogle Scholar
- 19.Kass M, Witkin A, Terzopoulos D (1988) Snakes: active contour models. Int J Comput Vis 1:321–331CrossRefGoogle Scholar
- 20.Sethian JA (1999) Level set methods and fast marching methods: evolving interfaces in computational geometry, fluid mechanics, computer vision, and materials science. Cambridge University Press, CambridgeGoogle Scholar
- 21.Sommer C, Straehle C, Koethe U, Hamprecht FA (2011) ilastik: interactive learning and segmentation toolkit. In: 2011 IEEE international symposium on biomedical imaging: from nano to macro, pp 230–233Google Scholar
- 22.Arganda-Carreras I, Cardona A, Kaynig V, Schindelin J (2011) Trainable weka segmentation. Fiji websiteGoogle Scholar
- 23.Serra J (1982) Image analysis and mathematical morphology. Academic Press, LondonGoogle Scholar
- 24.Soille P (2003) Morphological image analysis: principles and applications, 2nd edn. Springer-Verlag, Berlin, GermanyGoogle Scholar
- 25.Lee J-S (1983) Digital image smoothing and the sigma filter. Comput Vis Graph Image Process 24:255–269. doi: http://dx.doi.org/10.1016/0734-189X(83)90047-6
- 26.Perona P, Malik J (1990) Scale-space filtering and edge detection using anisotropic diffusion. IEEE Trans Pattern Anal Mach Intell 12:629–639CrossRefGoogle Scholar
- 27.Tomasi C, Manduchi R (1998) Bilateral filtering for gray and color images. In: Sixth Int. Conf. Comput. Vis. pp 839–846Google Scholar
- 28.Smith SM, Brady JM (1997) SUSAN—a new approach to low level image processing. Int J Comput Vis 23:45–78CrossRefGoogle Scholar
- 29.Buades A, Coll B, Morel J-M (2005) A review of image denoising algorithms, with a new one. Multiscale Model Simul 4:490–530CrossRefGoogle Scholar
- 30.Kervrann C, Boulanger J (2006) Optimal spatial adaptation for patch-based image denoising. IEEE Trans Image Process 15:2866–2878CrossRefPubMedGoogle Scholar
- 31.Wallace W, Schaefer LH, Swedlow JR (2001) A workingperson’s guide to deconvolution in light microscopy. Biotechniques 31:1076–1097PubMedGoogle Scholar
- 32.Cannell MB, McMorland A, Soeller C (2006) Image enhancement by deconvolution. In: Pawley BJ (ed) Handb. Biol. Confocal Microsc. Springer, Boston, MA, pp 488–500CrossRefGoogle Scholar
- 33.Vincent L (1993) Morphological grayscale reconstruction in image analysis: applications and efficient algorithms. IEEE Trans Image Process 2:176–201CrossRefPubMedGoogle Scholar
- 34.Breen EJ, Jones R (1996) Attribute openings, thinnings, and granulometries. Comput Vis Image Underst 64:377–389. doi: http://dx.doi.org/10.1006/cviu.1996.0066
- 35.Jaccard P (1912) The distribution of the flora in the alpine zone. New Phytol 11:37–50CrossRefGoogle Scholar
- 36.Dice LR (1945) Measures of the amount of ecologic association between species. Ecology 26:297–302CrossRefGoogle Scholar
- 37.Lehmussola A, Ruusuvuori P, Selinummi J et al (2007) Computational framework for simulating fluorescence microscope images with cell populations. IEEE Trans Med Imaging 26:1010–1016. doi: 10.1109/TMI.2007.896925 CrossRefPubMedGoogle Scholar
- 38.Svoboda D, Kozubek M, Stejskal S (2009) Generation of digital phantoms of cell nuclei and simulation of image formation in 3D image cytometry. Cytom A 75:494–509. doi: 10.1002/cyto.a.20714 CrossRefGoogle Scholar
- 39.Murphy RF (2016) Building cell models and simulations from microscope images. Methods 96:33–39. doi: 10.1016/j.ymeth.2015.10.011 CrossRefPubMedGoogle Scholar
- 40.Eils R, Dietzel S, Bertin E et al (1996) Three-dimensional reconstruction of painted human interphase chromosomes: active and inactive X chromosome territories have similar volumes but differ in shape and surface structure. J Cell Biol 135:1427–1440CrossRefPubMedGoogle Scholar
- 41.Pawley JB (2006) Points, pixels, and gray levels: digitizing image data. In: Pawley BJ (ed) Handb. Biol. Confocal Microsc. Springer, Boston, MA, pp 59–79CrossRefGoogle Scholar
- 42.Sheppard CJR, Gan X, Gu M, Roy M (2006) Signal-to-noise ratio in confocal microscopes. In: Pawley BJ (ed) Handb. Biol. Confocal Microsc. Springer, Boston, MA, pp 442–452CrossRefGoogle Scholar
- 43.Žunić J, Hirota K, Rosin PL (2010) A Hu moment invariant as a shape circularity measure. Pattern Recognit 43:47–57. doi: http://dx.doi.org/10.1016/j.patcog.2009.06.017
- 44.Xue J-H, Zhang Y-J (2012) Ridler and Calvard’s, Kittler and Illingworth’s and Otsu’s methods for image thresholding. Pattern Recognit Lett 33:793–797. doi: 10.1016/j.patrec.2012.01.002 CrossRefGoogle Scholar
- 45.Bassel GW, Stamm P, Mosca G et al (2014) Mechanical constraints imposed by 3D cellular geometry and arrangement modulate growth patterns in the Arabidopsis embryo. Proc Natl Acad Sci U S A 111:8685–8690CrossRefPubMedPubMedCentralGoogle Scholar
- 46.Maška M, Ulman V, Svoboda D et al (2014) A benchmark for comparison of cell tracking algorithms. Bioinformatics 30:1609–1617. doi: 10.1093/bioinformatics/btu080 CrossRefPubMedPubMedCentralGoogle Scholar