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An Image-Processing Tool for Size and Shape Analysis of Manufactured Irregular Polyethylene Microparticles
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Scientists developed a free, automated image-processing tool that can quickly analyze microscope images to count and measure irregularly shaped microplastic particles, calculating their size, shape, and distribution. Traditional methods require manually counting particles under a microscope, which is slow and impractical for large samples. Better tools for measuring microplastic contamination help researchers more accurately assess how much plastic pollution exists in water and soil that affects human exposure.
Microplastics (MPs) pose a significant risk to humans and animals due to their ability to absorb, adsorb, and desorb organic pollutants. MPs catchment from either sediments or water bodies is crucial for risk assessment, but fast and effective particle quantification of irregularly shaped particles is only marginally addressed. Many studies used microscopy methods to count MP particles, which are tedious for large sample sizes. Alternatively, this work presents an algorithm developed in the free software GNU Octave to analyze microscope images of MP particles with variable sizes and shapes. The algorithm can detect and distinguish different particles, compensate for uneven illumination and low image contrast, find high-contrast areas, unify edge regions, and fill the remaining pixels of stacked particles. The fully automatic algorithm calculates shape parameters such as convexity, solidity, reciprocal aspect ratio, rectangularity, and the Feret major axis ratio and generates the particle size distribution. The study tested low-density polyethylene particles with sizes of 50–100 µm and 200–300 µm. A scanning electron microscope image series analyzed with Octave was compared to a manual evaluation using ImageJ. Although the fully automatic algorithm did not identify all particles, the comprehensive tests demonstrate a qualitatively accurate particle size and shape monitoring applicable to any MPs, which processes larger data sets in a short time and is compatible with MATLAB-based codes.
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Scientists have created a new software tool that automatically spots and measures microplastic particles in microscope images, replacing the slow, error-prone process of counting them by hand. This matters because faster, more consistent microplastic detection could help researchers better track how much of this pollution is in our water, food, and environment, an important step toward understanding potential health risks. Note that this paper introduces the tool itself rather than new findings about microplastics' health effects.
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Researchers developed a flatbed scanner-based system paired with open-source image analysis software to speed up the counting, measuring, and classification of microplastics in samples, reducing the time required compared to manual microscopy. The affordable system makes high-throughput microplastic analysis more accessible to laboratories with limited resources.
A Handy Open-Source Application Based on Computer Vision and Machine Learning Algorithms to Count and Classify Microplastics
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An open-source computer vision application was developed to automatically count and classify microplastics in microscopy images, achieving accuracy comparable to manual counting while processing samples orders of magnitude faster, offering the scientific community a free tool to reduce the bottleneck of tedious visual microplastic enumeration.
Microplastic Spot Analyzer: Automated Image-Based Detection and Quantification of Microplastic Particles
AI summary Read the abstract
Scientists have created a new software tool that automatically spots and measures microplastic particles in microscope images, replacing the slow, error-prone process of counting them by hand. This matters because faster, more consistent microplastic detection makes it easier for researchers to study how much of this material is in our water, food, and environment, a key step toward understanding its potential health risks. Note that this paper introduces the tool itself rather than new findings about microplastics' health effects.
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Researchers developed and validated an open-source image analysis procedure for measuring morphological characteristics of microplastic items identified in fish organ samples, using manually set edge points in digital microscope images and comparison against commercial MotiConnect software. The proposed workflow enabled accurate calculation of shape descriptors such as length, width, and item area, offering a cost-effective alternative for routine laboratory microplastic morphological analysis.
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