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首页> 外文会议>Advanced Biomedical and Clinical Diagnostic Systems IV; Progress in Biomedical Optics and Imaging; vol.7 no.3 >Bacterial phenotype identification using Zernike moment invariants
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Bacterial phenotype identification using Zernike moment invariants

机译：使用Zernike矩不变量的细菌表型鉴定

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摘要

Pathogenic bacterial contamination in food products is costly to the public and to industry. Traditional methods for detection and identification of major food-borne pathogens such as Listeria monocytogenes typically take 3-7 days. Herein, the use of optical scattering for rapid detection, characterization, and identification of bacteria is proposed. Scatter patterns produced by the colonies are recognized without the need to use any specific model of light scattering on biological material. A classification system was developed to characterize and identify the scatter patterns obtained from colonies of various species of Listeria. The proposed classification algorithm is based on Zernike moment invariants (features) calculated from the scatter images. It has also been demonstrated that even a simplest approach to multivariate analysis utilizing principal component analysis paired with clustering or linear discriminant analysis can be successfully used to discriminate and classify feature vectors computed from the bacterial scatter patterns.
机译：食品中的致病细菌污染对公众和工业而言都是昂贵的。用于检测和识别主要食源性病原体（例如单核细胞增生李斯特菌）的传统方法通常需要3到7天。本文中，提出了使用光学散射来快速检测，表征和鉴定细菌。无需使用任何特定的生物材料上光散射模型即可识别菌落产生的散射图样。开发了分类系统以表征和鉴定从各种李斯特菌属菌落获得的散射模式。所提出的分类算法基于从散射图像计算出的Zernike矩不变性（特征）。还已经证明，使用主成分分析与聚类或线性判别分析相结合的最简单的多元分析方法也可以成功地用于区分和分类从细菌散布图计算出的特征向量。

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