Mirzaei Behzad, Nezamabadi-Pour Hossein, Raoof Amir, Nikpeyman Vahid, de Vries Enno, Derakhshani Reza
Intelligent Data Processing Laboratory (IDPL), Department of Electrical Engineering, Shahid Bahonar University of Kerman, Kerman 76169-13439, Iran.
Department of Earth Sciences, Utrecht University, 3584 CB Utrecht, The Netherlands.
Sensors (Basel). 2024 Aug 10;24(16):5180. doi: 10.3390/s24165180.
Over recent decades, natural and artificial colloids, as well as nanoparticles, have been increasingly used in various applications. Consequently, with this rising consumption, surface and subsurface environments are more exposed to these particles. The presence of these particles and the colloid-facilitated transport of microorganisms, the interactions between dissolved contaminants and mobile colloids in porous media, and the fate and transport of colloids through groundwater-one of the primary sources of water supply for human societies-have attracted extensive research. This study investigates the performance of several image processing methods in the field of colloid detection, which is a prerequisite for the subsequent steps in porous media research. We employed four different categories of image processing approaches on microscopy images-segmentation-based methods, background-detection-based methods, filter-based methods, and morphology-based methods-to conduct the detection process of colloids. Eight methods were applied and subsequently analyzed in terms of their drawbacks and advantages to determine the best ones in this domain. Finally, we proposed an ensemble approach that leverages the strengths of the three best methods using a majority vote to detect colloids more accurately. In experiments, Precision, Recall, -measure, and TCR criteria were considered as evaluation tools. Experimental results demonstrate the high accuracy of image processing methods in recognizing colloids. Among all these methods, morphology-based methods were the most successful, achieving the best detection performance and improving the limited distinguishing features of small colloids. Moreover, our ensemble approach, achieving perfect scores across all evaluation criteria, highlights its superiority compared with other detection methods.
在最近几十年里，天然和人工胶体以及纳米颗粒在各种应用中得到了越来越广泛的使用。因此，随着这种使用量的不断增加，地表和地下环境更多地暴露于这些颗粒之中。这些颗粒的存在、胶体促进的微生物传输、多孔介质中溶解污染物与移动胶体之间的相互作用，以及胶体在地下水中的归宿和传输（地下水是人类社会主要的供水来源之一），都吸引了广泛的研究。本研究调查了几种图像处理方法在胶体检测领域的性能，这是多孔介质研究后续步骤的一个先决条件。我们在显微镜图像上采用了四类不同的图像处理方法——基于分割的方法、基于背景检测的方法、基于滤波器的方法和基于形态学的方法——来进行胶体的检测过程。应用了八种方法，随后对其优缺点进行了分析，以确定该领域中最佳的方法。最后，我们提出了一种集成方法，利用三种最佳方法的优势，通过多数投票来更准确地检测胶体。在实验中，精度、召回率、F1值和TCR标准被用作评估工具。实验结果证明了图像处理方法在识别胶体方面的高精度。在所有这些方法中，基于形态学的方法最为成功，实现了最佳的检测性能，并改善了小胶体有限的区分特征。此外，我们的集成方法在所有评估标准上都取得了满分，突出了其与其他检测方法相比的优越性。
