Image recognition of juvenile colonies of pathogenic microorganisms in the culture based microbiological method implemented in bioMEMS device for express species identification

Y. Gvozdev, T. Zimina, L. Kraeva, G. N. Hamdulaeva
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引用次数: 1

Abstract

In this paper an image recognition method for microbiological analysis of species is described. This method is developed for use in bioMEMS hybrid laboratory-on-a-chip for total microbiological analysis and is aimed at recognition of colonies of microorganisms for express species identification and sorting. The described approach is based on the Histograms of Oriented Gradients (HOG) method, developed earlier. A principle of this technique is the computation of intensity gradients directions in local areas of image and determination of objects affinity using special classifier. The method is widely used for people identification, where it proved to be very efficient. In microbiology it appeared to be not as successful, because images of single microorganisms and their colonies are too similar. Here an advanced version of HOG is presented, which enabled a number of microbiological species to be identified. The method comprises the following stages: 1) Initial image acquisition; 2) Search and retrieval of the colony; 3) Simplification of the image, transition to local gradients of the grey; 4) Processing with threshold noise filter; 5) Fourier-transform of local gradients of the grey; 6) Formation of classifier by teaching the expert system by comparison with reference images. The described method is applied for recognizing colonies at earlier stages of growth (juvenile colonies) incorporating about 1000 cells. A sample of investigated microorganisms comprised 18 species from 8 different genera.
基于培养微生物学方法的病原微生物幼菌落图像识别在bioMEMS设备上实现,用于快速物种识别
本文介绍了一种用于物种微生物分析的图像识别方法。该方法是开发用于生物ems混合实验室芯片上的总微生物学分析,旨在识别菌落的微生物进行快速物种鉴定和分类。所描述的方法是基于之前开发的定向梯度直方图(HOG)方法。该技术的一个原理是计算图像局部区域的强度梯度方向,并使用特殊的分类器确定目标的亲和力。该方法被广泛应用于人物识别,并被证明是非常有效的。在微生物学方面,它似乎不那么成功,因为单个微生物及其菌落的图像太相似了。这里提出了一个先进版本的HOG,它使许多微生物物种得以识别。该方法包括以下几个阶段:1)初始图像采集;2)菌落的搜索与检索;3)对图像进行简化,过渡到灰度的局部梯度;4)阈值噪声滤波处理;5)灰度局部梯度的傅里叶变换;6)通过与参考图像的对比,训练专家系统形成分类器。所述方法用于识别包含约1000个细胞的生长早期阶段的菌落(幼菌落)。所调查的微生物样本包括来自8个不同属的18种。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
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