Centro de investigación CITIC, Universidade da Coruña, 15071 A Coruña, Spain.
Grupo VARPA, Instituto de Investigación Biomédica de A Coruña (INIBIC), Universidade da Coruña, 15006 A Coruña, Spain.
Sensors (Basel). 2020 Nov 23;20(22):6704. doi: 10.3390/s20226704.
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.
水安全和质量可能会受到产毒浮游植物物种的扩散的影响，因此需要对水源进行持续监测。这一分析涉及这些物种的识别和计数，这需要广泛的经验和知识。这些任务的自动化是非常可取的，因为它可以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，我们提出了一种用于自动分析使用常规显微镜采集的水样中浮游植物的数字图像的方法。具体来说，我们提出了一种新颖的、全自动的方法，使用经典的计算机视觉算法来检测和分割这些图像中的浮游植物标本。得益于一种新颖的融合算法，所提出的方法能够正确地检测稀疏的浮游植物群体作为单个浮游植物候选物，并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本中的其他图像对象（如矿物质、气泡或碎屑）区分开来，该方法利用了纹理和颜色特征。我们的初步实验表明，所提出的方法提供了令人满意和准确的结果。
