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Image processing and classification algorithm for yeast cell morphology in a microfluidic chip

来自 Semantic Scholar

阅读量：

423

作者：

YY Bo，C Elbuken，CL Ren，JP Huissoon

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摘要：

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.

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关键词：

image processing yeast pattern recognition microfluidic cytometry

DOI：

10.1117/1.3589100

被引量：

1890

年份：

2011

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来源期刊

Journal of Biomedical Optics

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2012
被引量：597

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