We can't find the internet
Attempting to reconnect
Something went wrong!
Hang in there while we get back on track
Papers
20 resultsShowing papers similar to Connected Component Labelling in the determination of morphometric features of microplastic particles in samples of different matrices
ClearConnected Component Labelling in the determination of morphometric features of microplastic particles in samples of different matrices
Researchers applied Connected Component Labeling (CCL) image analysis to optimize microscopic quantification of microplastic particles in Baltic Sea fish tissues and organs, combining optical microscopy for size and shape determination with FT-IR spectrometry for polymer identification.
Application of Pattern Recognition and Computer Vision Tools to Improve the Morphological Analysis of Microplastic Items in Biological Samples
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.
Automatic Counting and Classification of Microplastic Particles
Researchers developed an automatic system for counting and classifying microplastic particles in marine samples, applying image analysis techniques to address the growing problem of plastic debris entering the food chain via marine species ingestion.
Implementation of an open source algorithm for particle recognition and morphological characterisation for microplastic analysis by means of Raman microspectroscopy
An automated particle recognition algorithm was implemented to speed up the identification and measurement of microplastics in Raman spectroscopy images. Automated analysis reduces the time and subjectivity involved in manual microplastic counting, improving research efficiency.
Deep learning based approach for automated characterization of large marine microplastic particles
A deep learning approach using Mask R-CNN was trained on 3,000 images of marine microplastic particles to automatically locate, classify, and segment particles by shape categories including fiber, fragment, pellet, and rod. The model achieved high accuracy and outperformed manual visual inspection for characterizing large marine microplastic datasets.
A Handy Open-Source Application Based on Computer Vision and Machine Learning Algorithms to Count and Classify Microplastics
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.
Proceeding the categorization of microplastics through deep learning-based image segmentation
Researchers developed a deep learning-based image segmentation method using Mask R-CNN to automatically identify and classify microplastic shapes in microscopic images, demonstrating a practical step toward standardized and automated microplastic categorization.
Machine learning enhanced machine vision system for micro-plastics particles classification
Researchers developed a machine learning-based classification system using fluorescence microscopy with Nile Red staining to identify and categorize microplastic types in environmental samples, aiming to provide a faster and more automated alternative to labor-intensive manual identification methods.
Microplastic and nanoplastic analysis methods, tests and reference materials
Researchers described a workflow combining a streamlined experimental setup with automated image analysis to quantify marine microplastic debris, addressing the limitations of labor-intensive manual counting methods that currently prevent scalable and consistent global plastic monitoring.
An Image-Processing Tool for Size and Shape Analysis of Manufactured Irregular Polyethylene Microparticles
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.
Microplastic Binary Segmentation with Resolution Fusion and Large Convolution Kernels
Researchers developed an improved machine-learning model to automatically detect and segment microplastic particles in images, achieving better accuracy than previous approaches by combining multi-resolution image analysis with large convolution kernels. Reliable automated detection tools are essential for scaling up microplastic monitoring, since manual identification is too slow and inconsistent for the volumes of environmental samples that need to be processed.
TUM-ParticleTyper: A detection and quantification tool for automated analysis of (Microplastic) particles and fibers
TUM-ParticleTyper is a new computer program that automatically detects and counts microplastic particles in microscope images, reducing the time and human effort needed for analysis. The tool can also flag particles for chemical identification, helping standardize microplastic research.
Identification Tools of Microplastics from Surface Water Integrating Digital Image Processing and Statistical Techniques
This study demonstrated that digital image analysis can automate and improve the characterization of microplastic particles collected from river water, capturing detailed shape, color, and size data that manual microscopy cannot easily achieve at scale. Better identification tools like this are essential for standardizing microplastic monitoring across different waterways and research groups.
Detection, counting and characterization of nanoplastics in marine bioindicators: a proof of principle study
Researchers demonstrated a proof-of-concept workflow for detecting and counting nanoplastic particles (below 1 µm) in marine invertebrate tissues using electron microscopy and spectroscopic confirmation, finding nanoplastics in marine bioindicator species and establishing a methodology for future monitoring programs.
2D imaging tools for harmonisation in plastic pollution data
Researchers evaluated four 2D imaging and image segmentation methodologies for measuring the morphological characteristics -- size, shape, and colour -- of mesoplastics and microplastics, aiming to harmonize physical characterization data across studies conducted in different marine environments.
Learning Single‐Cell Distances from Cytometry Data
This computational paper developed machine learning methods to measure distances between individual cells in flow cytometry data. While a bioinformatics paper, the methodology is potentially applicable to automated classification of microplastic particles in environmental samples.
Weka Model for automated microplastics segmentation in ImageJ
This is a machine-learning model file (Weka segmentation for ImageJ) used to automate the identification of microplastic particles in microscope images — a software tool rather than a primary research article.
Digital Image Analysis and Multivariate Data Analysis as Tools for the Identification of Microplastics in Surface Waters: The Case of the Vistula River (Central Europe)
Researchers demonstrated digital image analysis combined with microscopy as a tool for identifying and characterizing microplastic particles from Vistula River surface water samples, performing exhaustive quantitative and qualitative evaluation of 2D and 3D morphology to characterize MP abundance and composition.
Computer vision segmentation model—deep learning for categorizing microplastic debris
Researchers developed a deep learning computer vision model for automatically categorizing beached microplastic debris from images. The segmentation model was trained to identify and classify different types of microplastic particles, reducing the need for time-consuming manual counting and laboratory analysis. The study suggests that automated image-based detection could enable more scalable and consistent monitoring of microplastic pollution along coastlines.
Detection of microplastics in fish using computed tomography and deep learning
CT scanning combined with deep learning neural networks enabled non-destructive, automated detection and localization of microplastics in fish with high accuracy, overcoming the contamination risk and time-consuming nature of conventional dissection-based methods.