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Microsystems, Electronics and Acoustics
ISSN 2523-4447 · e‑ISSN 2523-4455 Open Access · CC BY-SA 4.0
Vol. 24 · Issue 5 · 2019 Oct 31, 2019 Electronic Systems and Signals

Automated Detection and Classification of the Diatom Microscopic Photo Images

MI
Maryna Viktorivna Ivanova Corresponding Національний технічний університет України "Київський політехнічний інститут імені Ігоря Сікорського" Ukraine
YV
Yurii Volodymyrovych Vuntesmeri Національний технічний університет України "Київський політехнічний інститут імені Ігоря Сікорського" Ukraine
LB
Liudmyla Mykolaivna Bukhtiiarova ДУ «Інститут еволюційної екології НАН України» Ukraine
Pages18-25 PublishedOct 31, 2019
PDF (Ukrainian)
VOL 24 · ISS 5 · 2019 View issue

Abstract

The diatoms (Bacillariophyta) are a species-rich and extremely widespread microalgae. Many diatom species require a fairly narrow range of ecological conditions (temperature, pH, salinity, saprobity, trophic level of the water etc.). They are very sensitive to changes of different water chemical parameters that allows to use of the diatoms as the indicators in monitoring of water quality as well as for the ecological reconstruction using bottom sediments. Different methods of the diatom analysis are based on the species composition and species amount in the preparations of plankton, benthos and epyphyton. However this analysis requires highly qualified staff and the manual processing of hundreds samples. The development of the computer tools for the automatic diatom species detection and identification is an actual task to improve the effectiveness of ecological studies and simplify the time-consuming routine work of the researchers. The image processing methods were investigated for the diatom species automatic identification in the light microscopic photos of the samples that were collected for the routine monitoring. The methods of segmentation of the images based on the threshold processing and morphological differentiation of the images were studied. The two-steps segmentation of the diatom photos was proposed. The method of artificially expanding the training dataset by sequential image transformation was applied. Based on the prepared training dataset, two realizations of convolutional neural networks were trained and their accuracy evaluated.

Keywords

#biomonitoring #Bacillariophyta #automated image processing #detection #classification #convolutional neural networks

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