Share Email Print

Journal of Biomedical Optics • Open Access

Image processing and classification algorithm for yeast cell morphology in a microfluidic chip
Author(s): Bo Yang Yu; Caglar Elbuken; Carolyn L. Ren; Jan Paul Huissoon

Paper Abstract

The study of yeast cell morphology requires consistent identification of cell cycle phases based on cell bud size. A computer-based image processing algorithm is designed to automatically classify microscopic images of yeast cells in a microfluidic channel environment. The images were enhanced to reduce background noise, and a robust segmentation algorithm is developed to extract geometrical features including compactness, axis ratio, and bud size. The features are then used for classification, and the accuracy of various machine-learning classifiers is compared. The linear support vector machine, distance-based classification, and k-nearest-neighbor algorithm were the classifiers used in this experiment. The performance of the system under various illumination and focusing conditions were also tested. The results suggest it is possible to automatically classify yeast cells based on their morphological characteristics with noisy and low-contrast images.

Paper Details

Date Published: 1 June 2011
PDF: 10 pages
J. Biomed. Opt. 16(6) 066008 doi: 10.1117/1.3589100
Published in: Journal of Biomedical Optics Volume 16, Issue 6
Show Author Affiliations
Bo Yang Yu, Univ. of Waterloo (Canada)
Caglar Elbuken, Univ. of Waterloo (Canada)
Carolyn L. Ren, Univ. of Waterloo (Canada)
Jan Paul Huissoon, Univ. of Waterloo (Canada)

© SPIE. Terms of Use
Back to Top