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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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