MULTI-CLASS SEGMENTATION OF HETEROGENEOUS AREAS IN BIOMEDICAL AND ENVIRONMENTAL IMAGES BASED ON THE ASSESSMENT OF LOCAL EDGE DENSITY

Imaging techniques employed in biomedical and ecological applications typically require complex equipment and experimental approaches, including sophisticated multispectral cameras, as well as physical markup of samples, altogether limiting their broad availability. Accordingly, computerized methods...

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Vydané v:International archives of the photogrammetry, remote sensing and spatial information sciences. Ročník XLVIII-2/W3-2023; s. 233 - 238
Hlavní autori: Sinitca, A. M., Lyanova, A. I., Kaplun, D. I., Zelenikhin, P. V., Imaev, R. G., Gafurov, A. M., Usmanov, B. M., Tishin, D. V., Kayumov, A. R., Bogachev, M. I.
Médium: Journal Article Konferenčný príspevok..
Jazyk:English
Vydavateľské údaje: Gottingen Copernicus GmbH 12.05.2023
Copernicus Publications
Predmet:
Adaptive algorithms
Algorithms
Archives & records
Chlorophyll
Deep learning
Density
Detection
Edge detection
Environmental science
Image segmentation
Imaging techniques
Remote sensing
Vegetation
ISSN:2194-9034, 1682-1750, 2194-9034
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Shrnutí:Imaging techniques employed in biomedical and ecological applications typically require complex equipment and experimental approaches, including sophisticated multispectral cameras, as well as physical markup of samples, altogether limiting their broad availability. Accordingly, computerized methods allowing to obtain similar information from images obtained in visible light spectrum with reasonable accuracy are of considerable interest. Edge detection methods are commonly used to find discriminating curves in image segmentation. Here we follow an alternative route and employ edge detection results as a separate metric characterizing local structural properties of the image. In turn, their characteristics such as density or orientation averaged in a gliding window are used as a virtual channel substituting multispectral imaging and/or physical markup of samples, and the following image segmentation procedures are performed by thresholding. In complex segmentation scenarios, a single fixed threshold often appears sufficient, and thus relevant adaptive multi-threshold algorithms are of interest, with slope difference distribution (SDD) thresholding algorithm representing a prominent example.
Bibliografia:ObjectType-Article-1
ObjectType-Feature-2
SourceType-Conference Papers & Proceedings-1
content type line 22
ISSN:2194-9034
1682-1750
2194-9034
DOI:10.5194/isprs-archives-XLVIII-2-W3-2023-233-2023