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utomatic Detection of Freshwater Phytoplankton Specimens in Conventional Microscopy Images
Sensors ( IF 3.9 ) Pub Date : 2020-11-23 , DOI: 10.3390/s20226704
David Rivas-Villar , José Rouco , Manuel G. Penedo , Rafael Carballeira , Jorge Novo

Water safety and quality can be compromised by the proliferation of toxin-producing phytoplankton species, requiring continuous monitoring of water sources. This analysis involves the identification and counting of these species which requires broad experience and knowledge. The automatization of these tasks is highly desirable as it would release the experts from tedious work, eliminate subjective factors, and improve repeatability. Thus, in this preliminary work, we propose to advance towards an automatic methodology for phytoplankton analysis in digital images of water samples acquired using regular microscopes. In particular,we propose a novel and fully automatic method to detect and segment the existent phytoplankton specimens in these images using classical computer vision algorithms.The proposed method is able to correctly detect sparse colonies as single phytoplankton candidates, thanks to a novel fusion algorithm, and is able to differentiate phytoplankton specimens from other image objects in the microscope samples (like minerals, bubbles or detritus) using a machine learning based approach that exploits texture and colour features. Our preliminary experiments demonstrate that the proposed method provides satisfactory and accurate results.

中文翻译:

常规显微镜图像中淡水浮游植物标本的自动检测

产生毒素的浮游植物种类的扩散会损害水的安全和质量,需要对水源进行连续监测。这种分析涉及对这些物种的鉴定和计数,这需要广泛的经验和知识。这些任务的自动化非常必要,因为这将使专家摆脱繁琐的工作,消除主观因素并提高可重复性。因此,在这项初步工作中,我们建议向使用常规显微镜采集的水样数字图像中浮游植物分析的自动方法前进。特别是,我们提出了一种新颖的,全自动的方法,可以使用经典的计算机视觉算法检测和分割这些图像中存在的浮游植物标本。借助一种新颖的融合算法,该方法能够正确地检测出稀疏菌落作为单个浮游植物的候选者,并且能够使用基于机器学习的方法,将浮游植物标本与显微镜样品中的其他图像对象(例如矿物质,气泡或碎屑)区分开利用纹理和颜色特征的方法。我们的初步实验表明,所提出的方法可提供令人满意且准确的结果。
更新日期:2020-11-23
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