Показати скорочену інформацію

dc.contributor.authorАндрікевич, С. А.uk
dc.contributor.authorТужанський, С. Є.uk
dc.contributor.authorAndrikevych, S.en
dc.contributor.authorTuzhanskyi, S.en
dc.date.accessioned2024-10-26T12:44:28Z
dc.date.available2024-10-26T12:44:28Z
dc.date.issued2024
dc.identifier.citationАндрікевич С. А., Тужанський С. Є. Методи сегментації оптичних зображень очного дна. Оптико-електроннi iнформацiйно-енергетичнi технологiї. 2024. Том 47, № 1. С. 155–165.uk
dc.identifier.issn1681-7893
dc.identifier.urihttps://ir.lib.vntu.edu.ua//handle/123456789/43397
dc.description.abstractThe paper presents a comparative analysis and evaluation of methods for segmenting optical fundus images in order to study their efficiency, accuracy, completeness, and computational complexity in Matlab. The methods analyzed are Otsu, adaptive thresholding, Watershed, K-means, maximum expectation algorithm (EM), and Frangi method. The features, advantages and disadvantages in the context of application for the diagnosis of fundus diseases are considereden
dc.description.abstractУ статті проводиться порівняльний аналіз та оцінювання методів сегментації оптичних зображень очного дна з метою дослідження їх ефективності, точності, повноти та обчислювальної складності у Matlab. Проаналізовані методи Otsu, адаптивного порогування, Watershed, K-середніх, алгоритм максимальної очікуваності (EM) та метод Франгі. Розглянуто особливості, переваги та недоліки в контексті застосування для діагностики захворювань очного днаuk
dc.language.isouk_UAuk_UA
dc.publisherВНТУuk
dc.relation.ispartofОптико-електроннi iнформацiйно-енергетичнi технологiї. №1 : 155–165.uk
dc.relation.urihttps://oeipt.vntu.edu.ua/index.php/oeipt/article/view/701
dc.subjectочне дноuk
dc.subjectMatlaben
dc.subjectметод сегментації оптичних зображеньuk
dc.subjectFundusen
dc.subjectoptical image segmentation methoden
dc.titleМетоди сегментації оптичних зображень очного днаuk
dc.title.alternativeOptical fundus image segmentation methodsen
dc.typeArticle, professional native edition
dc.typeArticle
dc.identifier.udc004.9
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dc.identifier.doihttps://doi.org/10.31649/1681-7893-2024-47-1-155-165
dc.identifier.orcidhttps://orcid.org/0000-0002-0185-7490


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