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Classification of Normal and Apoptotic Cells from Fluorescence Microscopy Images Using Generalized Polynomial Chaos and Level Set Function

Published online by Cambridge University Press:  04 May 2016

Yuncheng Du ,
Hector M. Budman  and
Thomas A. Duever
Yuncheng Du
Affiliation:
Department of Chemical Engineering, University of Waterloo, 200 University Ave W, Waterloo, ON, Canada, N2L 3G1
Hector M. Budman*
Affiliation:
Department of Chemical Engineering, University of Waterloo, 200 University Ave W, Waterloo, ON, Canada, N2L 3G1
Thomas A. Duever
Affiliation:
Department of Chemical Engineering, University of Waterloo, 200 University Ave W, Waterloo, ON, Canada, N2L 3G1
*
*Corresponding author. hbudman@uwaterloo.ca
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Abstract

Accurate automated quantitative analysis of living cells based on fluorescence microscopy images can be very useful for fast evaluation of experimental outcomes and cell culture protocols. In this work, an algorithm is developed for fast differentiation of normal and apoptotic viable Chinese hamster ovary (CHO) cells. For effective segmentation of cell images, a stochastic segmentation algorithm is developed by combining a generalized polynomial chaos expansion with a level set function-based segmentation algorithm. This approach provides a probabilistic description of the segmented cellular regions along the boundary, from which it is possible to calculate morphological changes related to apoptosis, i.e., the curvature and length of a cell’s boundary. These features are then used as inputs to a support vector machine (SVM) classifier that is trained to distinguish between normal and apoptotic viable states of CHO cell images. The use of morphological features obtained from the stochastic level set segmentation of cell images in combination with the trained SVM classifier is more efficient in terms of differentiation accuracy as compared with the original deterministic level set method.

Keywords

live-cell imagingbioinformaticsapoptosis detectionstochastic image segmentation
Type
Technique and Instrumentation Development
Information
Microscopy and Microanalysis , Volume 22 , Issue 3 , June 2016 , pp. 475 - 486
Copyright
Copyright © Microscopy Society of America 2016

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References

Aurum, A. & Wohlin, C. (2003). The fundamental nature of requirements engineering activities as a decision making process. Inf Softw Technol 45(14), 945–954.Google Scholar
Burgers, C.J. (1998). A tutorial on support vector machines for pattern recognition. Data Min Knowl Discov 2(2), 121–167.Google Scholar
Carpenter, A.E., Jones, T.R., Lamprecht, M.R., Clarke, C., Kang, I-H., Friman, O., Guertin, D.A., Change, J-H., Lindquist, R.A., Moffat, J., Golland, P. & Sabatini, D.M. (2006). CellProfiler: Image analysis software for identifying and quantifying cell phenotypes. Genome Biol 7(10), R100.Google Scholar
Chan, T.F. & Shen, J. (2005). Image Processing and Analysis: Variational, PDE, Wavelet, and Stochastic Methods. Philadelphia, PA: The Society for Industrial and Applied Mathematics.Google Scholar
Chan, T.F. & Vese, L.A. (2001). Active contours without edges. IEEE Trans Image Process 10(2), 266–277.Google Scholar
Debusschere, B.J., Najm, H.B., Pebay, P.P., Knio, O.M., Ghanem, R.G. & Le Maitre, O.P. (2004). Numerical challenges in the use of polynomial chaos representations for stochastic processes. SIAM J Sci Comput 26(2), 698–719.Google Scholar
Getreuer, P. (2012). Chan-Vese segmentation. Image Process Online 2(1), 214–224.Google Scholar
Juneau, P.-M., Garnier, A. & Duchesne, C. (2013). Selection and tuning of a fast and simple phase contrast microscopy image segmentation algorithm for measuring myoblast growth kinetics in an automated manner. Microsc Microanal 19(4), 856–866.Google Scholar
Kybic, J. (2010). Bootstrap resampling for image registration uncertainty estimation without ground truth. IEEE Trans Image Process 19(1), 64–73.Google Scholar
Li, C., Kao, C.-Y., Gore, J.C. & Ding, Z. (2008). Minimization of region scalable fitting energy for image segmentation. IEEE Trans Image Process 17(10), 1940–1949.Google Scholar
Mercille, S. & Massie, B. (1994). Induction of apoptosis in nutrient deprived cultures of hybridoma and myeloma cells. Biotechnol Bioeng 44(9), 1140–1154.Google Scholar
Meshram, M., Naderi, S., McConkey, B., Budman, H., Scharer, J. & Ingalls, B. (2011). Population based modeling of the progression of apoptosis in mammalian cell culture. Biotechnol Bioeng 109(5), 1193–1204.Google Scholar
Osher, S. & Sethian, J.A. (1988). Fronts propagating with curvature dependent speed: Algorithms based on Hamilton Jacobi formulation. J Comput Phys 79(1), 12–49.Google Scholar
Patz, T. & Preusser, T. (2014). Segmentation of stochastic images using level-set propagation with uncertain speed. J Math Imaging Vis 48(3), 467–487.10.1007/s10851-013-0421-zGoogle Scholar
Preusser, T., Scharr, H., Krajsek, K. & Kirby, R.M. (2008). Building blocks for computer vision with stochastic partial differential equations. Int J Comput Vis 80(3), 375–405.Google Scholar
Schneider, C.A., Rasband, W.S. & Eliceiri, K.W. (2012). NIH image to ImageJ: 25 years of image analysis. Nat Methods 9(1), 671–675.Google Scholar
Stefanou, G., Nouy, A. & Clement, A. (2009). Identification of random shapes from images through polynomial chaos expansion of random level-set functions. Int J Numer Methods Eng 79(2), 127–155.Google Scholar
Theriault, D.H., Walker, M.L., Wong, J.Y. & Betke, M. (2012). Cell morphology classification and clutter mitigation in phase contrast microscopy images using machine learning. Mach Vis Appl 23(4), 659–673.Google Scholar
Veredas, F., Mesa, H. & Morente, L. (2010). Binary tissue classification on wound images with neural networks and Bayesian classifiers. IEEE Trans Med Imaging 29(2), 410–427.Google Scholar
Waters, J.C. (2009). Accuracy and precision in quantitative fluorescence microscopy. J Cell Biol 185(7), 1135–1148.Google Scholar
Xiu, D. (2010). Numerical Methods for Stochastic Computations: A Spectral Method Approach. Princeton, NJ: Princeton University Press.Google Scholar
Xiu, D. & Karniadakis, G.E. (2002). The Wiener-Askey polynomial chaos for stochastic differential equations. SIAM J Sci Comput 24(2), 619–644.Google Scholar
Yin, Z., Bise, R., Chen, M. & Kanade, T. (2010). Cell segmentation in microscopy imagery using a bag of local Bayesian classifiers. In IEEE International Symposium on Biomedical Imaging: From Nano to Macro, Niessen, W. & Meijerring, E. (Eds.), pp. 125–128. Rotterdam, The Netherlands: IEEE.Google Scholar
Zhao, H., Chan, T., Merriman, B. & Osher, S. (1996). A variational level set approach to multiphase motion. J Comput Phys 127(1), 179–195.Google Scholar
Ziadi, A., Maldague, X., Saucier, L., Duchesne, C. & Gosselin, R. (2012). Visible and near-infrared light transmission: a hybrid imaging method for non-destructive meat quality evaluation. Infrared Physics & Technology 55(5), 412–420.Google Scholar