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Адаптация метода границ Канни для подсчета количества бактерий на изображениях, получаемых с помощью цифрового микроскопа

Адаптация метода границ Канни для подсчета количества бактерий на изображениях, получаемых с помощью цифрового микроскопа

Abstract

Предложен модифицированный алгоритм Канни для обнаружения и подсчета бактерий на микроскопических изображениях. Подход включает предварительную обработку с контрастной адаптивной эквализацией гистограммы, применение алгоритма Канни, морфологические операции для удаления шумов и артефактов, а также поиск замкнутых контуров и вычисление центров бактерий для их подсчета. На основе предложенного метода продемонстрирована улучшенная точность обнаружения и подсчета бактерий. A modified Canny algorithm is proposed for detecting and counting bacteria in microscopic images. The approach includes preprocessing with contrast limited adaptive histogram equalization, applying the Canny algorithm, morphological operations for noise and artifact removal, as well as finding closed contours and calculating bacteria centers for counting. The proposed method demonstrates improved accuracy in detecting and counting bacteria.

Country
Belarus
Keywords

Microscopic images, Bacterial counting, Подсчет бактерий, Обработка изображений, Микроскопические изображения, CLAHE, Морфологические операции, Contour detection, Алгоритм Канни, Image processing, Edge detection, Morphological operations, Обнаружение границ, Поиск контуров, Canny algorithm

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selected citations
These citations are derived from selected sources.
This is an alternative to the "Influence" indicator, which also reflects the overall/total impact of an article in the research community at large, based on the underlying citation network (diachronically).
BIP!Citations provided by BIP!
popularity
This indicator reflects the "current" impact/attention (the "hype") of an article in the research community at large, based on the underlying citation network.
BIP!Popularity provided by BIP!
influence
This indicator reflects the overall/total impact of an article in the research community at large, based on the underlying citation network (diachronically).
BIP!Influence provided by BIP!
impulse
This indicator reflects the initial momentum of an article directly after its publication, based on the underlying citation network.
BIP!Impulse provided by BIP!
0
Average
Average
Average
Green