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

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...

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Bibliographic Details
Published in:Microscopy and microanalysis Vol. 22; no. 3; pp. 475 - 486
Main Authors: Du, Yuncheng, Budman, Hector M., Duever, Thomas A.
Format: Journal Article
Language:English
Published: New York, USA Cambridge University Press 01.06.2016
Oxford University Press
Subjects:
Algorithms
Animals
Apoptosis
Automation
Cells
CHO Cells
Cricetinae
Cricetulus
Cytological Techniques - methods
Fluorescence
Fluorescence microscopy
Image Processing, Computer-Assisted
Microscopy
Microscopy, Fluorescence
Morphology
Ovaries
Partial differential equations
Physiology
Support Vector Machine
Support vector machines
Technique and Instrumentation Development
stochastic image segmentation
apoptosis detection
live-cell imaging
bioinformatics
ISSN:1431-9276, 1435-8115, 1435-8115
Online Access:Get full text
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Summary: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.
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ISSN:1431-9276
1435-8115
1435-8115
DOI:10.1017/S1431927616000702