Digital Image Analysis
Digital Image Analysis - Related Videos
06:02
Measuring Stolons and Rhizomes of Turfgrasses Using a Digital Image Analysis System
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Cited by 2 •
2019
A software-based image analysis system provides an alternative method to study the morphology of stoloniferous and rhizomatous species. This protocol permits measurement of the length and diameter of stolons and rhizomes and can be applied to samples with a large amount of biomass and to a wide variety of species.
07:46
Platform for Quantitative Detection of Endometrial Immune Cells Based on Immunohistochemistry and Digital Image Analysis
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2023
Here, a digital immunohistochemistry image analysis platform was developed and validated to quantitatively analyze the endometrial immune cells of patients with recurrent miscarriages in the window of implantation.
07:50
Measuring Local Tissue Strains in Tendons via Open-Source Digital Image Correlation
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Cited by 1 •
2023
This paper describes an open-source digital image correlation algorithm for measuring local 2D tissue strains within tendon explants. The accuracy of the technique has been validated using multiple techniques, and it is available for public use.
16:21
Quantifying Fish Swimming Behavior in Response to Acute Exposure of Aqueous Copper Using Computer Assisted Video and Digital Image Analysis
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Cited by 29 •
2016
Measuring the impacts of environmental contaminants on fish behavior is often subjective and challenging particularly when dealing with sublethal endpoints. We describe methods including video technology to quantify swimming behavior of early life stage white sturgeon (Acipenser transmontanus) during and after 96 hr acute exposures to various concentrations of copper.
09:29
Using Digital Image Correlation to Characterize Local Strains on Vascular Tissue Specimens
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Cited by 20 •
2016
We describe the use of digital image correlation to characterize the local surface strain field on vascular tissue samples subjected to uniaxial tensile testing. These measurements facilitate precise quantification of the sample mechanical response and the generation of constitutive stress-strain relations.
FAQs
Segmentation divides image data into distinguishable regions so researchers can separate materials or conditions before measuring them. In environmental work, this may help distinguish vegetation, water bodies, land-cover categories, or visible pollution indicators. The quality of segmentation affects later feature extraction and classification because poorly separated regions can reduce the reliability of mapped patterns and quantitative comparisons.
These properties provide different ways to distinguish environmental features that may appear similar in an image. Color can separate visible conditions, spectral response can differentiate materials, texture can describe surface variation, and spatial arrangement can reveal how features are organized across an area. Combining these characteristics supports more informative classification of land cover, habitats, and environmental change.
Classification assigns image regions or pixels to meaningful categories using extracted characteristics such as color, spectral response, texture, or spatial arrangement. This converts image measurements into interpretable environmental information, including mapped land-cover types or identified water bodies. The resulting categories allow researchers to compare conditions across locations and examine how ecosystems or landscapes change over time.
A typical workflow begins with preprocessing the image data, followed by segmentation to separate relevant regions. Researchers then extract measurable features and apply classification to distinguish materials or conditions. The workflow can be used with digital photographs, satellite imagery, and other visual data, producing quantitative information suitable for mapping, monitoring, and comparison across places or time periods.
Digital photographs, satellite imagery, and other visual data can all provide inputs for environmental analysis. Photographs may document visible local conditions, while satellite imagery supports examination of patterns across broader locations. Regardless of source, the images are converted into pixel-based data and processed into measurable characteristics that can support environmental mapping and monitoring.
The method is useful when researchers need repeatable measurements across locations or time. Applications described for environmental studies include mapping land cover, monitoring vegetation and water bodies, quantifying habitat change, and identifying pollution indicators. These results can help assess ecosystem dynamics and provide evidence for conservation planning, resource management, and environmental policy decisions.