KFU. Card publication. Multi-class segmentation of heterogeneous areas in biomedical and environmental images based on the assessment of local edge density

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

Form of presentation

Articles in international journals and collections

Year of publication

2023

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Authors, employees of KFU

Bogachev Mikhail Igorevich, author

Gafurov Artur Maratovich, author

Zelenikhin Pavel Valerevich, author

Kayumov Ayrat Rashitovich, author

Tishin Denis Vladimirovich, author

Usmanov Bulat Mansurovich, author

Other authors

Bogachev Mikhail Igorevich, author

Kaplun Dmitriy Ilich, author

Lyyanova Asiya , author

Sinica Aleksandr Mikhaylovich, author

Authors, students KFU

Imaev Rasul Gabdrafikovich, author

Bibliographic description in the original language

Sinitca A.M. Multi-class segmentation of heterogeneous areas in biomedical and environmental images based on the assessment of local edge density / A.M. Sinitca, A.I. Lyanova, D.I. Kaplun, P.V. Zelenikhin, R.G. Imaev, A.M. Gafurov, B.M. Usmanov, D.V. Tishin, A.R. Kayumov, M.I. Bogachev // International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences - ISPRS Archives. - 2023. - Vol.48, Is.2/W3-2023. - P.233-238.

Annotation

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.

Keywords

multispectral images, remote sensing, segmentation, patchiness, edge density

The name of the journal

International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences - ISPRS Archives

URL

https://isprs-archives.copernicus.org/articles/XLVIII-2-W3-2023/233/2023/

Please use this ID to quote from or refer to the card

https://repository.kpfu.ru/eng/?p_id=286443&p_lang=2

Resource files

File name	Size (MB)	Format
F_isprs_archives_XLVIII_2_W3_2023_233_2023.pdf	3,62	pdf	show / download

Full metadata record

Field DC	Value	Language
dc.contributor.author	Bogachev Mikhail Igorevich	ru_RU
dc.contributor.author	Gafurov Artur Maratovich	ru_RU
dc.contributor.author	Zelenikhin Pavel Valerevich	ru_RU
dc.contributor.author	Kayumov Ayrat Rashitovich	ru_RU
dc.contributor.author	Tishin Denis Vladimirovich	ru_RU
dc.contributor.author	Usmanov Bulat Mansurovich	ru_RU
dc.contributor.author	Bogachev Mikhail Igorevich	ru_RU
dc.contributor.author	Kaplun Dmitriy Ilich	ru_RU
dc.contributor.author	Lyyanova Asiya	ru_RU
dc.contributor.author	Sinica Aleksandr Mikhaylovich	ru_RU
dc.contributor.author	Imaev Rasul Gabdrafikovich	ru_RU
dc.date.accessioned	2023-01-01T00:00:00Z	ru_RU
dc.date.available	2023-01-01T00:00:00Z	ru_RU
dc.date.issued	2023	ru_RU
dc.identifier.citation	Sinitca A.M. Multi-class segmentation of heterogeneous areas in biomedical and environmental images based on the assessment of local edge density / A.M. Sinitca, A.I. Lyanova, D.I. Kaplun, P.V. Zelenikhin, R.G. Imaev, A.M. Gafurov, B.M. Usmanov, D.V. Tishin, A.R. Kayumov, M.I. Bogachev // International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences - ISPRS Archives. - 2023. - Vol.48, Is.2/W3-2023. - P.233-238.	ru_RU
dc.identifier.uri	https://repository.kpfu.ru/eng/?p_id=286443&p_lang=2	ru_RU
dc.description.abstract	International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences - ISPRS Archives	ru_RU
dc.description.abstract	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.	ru_RU
dc.language.iso	ru	ru_RU
dc.subject	multispectral images	ru_RU
dc.subject	remote sensing	ru_RU
dc.subject	segmentation	ru_RU
dc.subject	patchiness	ru_RU
dc.subject	edge density	ru_RU
dc.title	Multi-class segmentation of heterogeneous areas in biomedical and environmental images based on the assessment of local edge density	ru_RU
dc.type	Articles in international journals and collections	ru_RU