Water Pollution Detection System Based on Fish Gills as a Biomarker

Kód výsledku v IS VaVaI

<a href="https://www.isvavai.cz/riv?ss=detail&h=RIV%2F61989100%3A27240%2F15%3A86099397" target="_blank" >RIV/61989100:27240/15:86099397 - isvavai.cz</a>

Výsledek na webu

<a href="http://dx.doi.org/10.1016/j.procs.2015.09.004" target="_blank" >http://dx.doi.org/10.1016/j.procs.2015.09.004</a>

DOI - Digital Object Identifier

<a href="http://dx.doi.org/10.1016/j.procs.2015.09.004" target="_blank" >10.1016/j.procs.2015.09.004</a>

Jazyk výsledku

angličtina

Název v původním jazyce

Water Pollution Detection System Based on Fish Gills as a Biomarker

Popis výsledku v původním jazyce

This article presents an automatic system for assessing water quality based on fish gills microscopic images. As fish gills are a good biomarker for assessing water quality, the proposed system uses fish gills microscopic images in order to detect water pollution. The proposed system consists of three phases; namely pre-processing, feature extraction, and classification phases. Since the shape is the main characteristic of fish gills microscopic images, the proposed system uses shape feature based on edge detection and wavelets transform for classifying the water-quality degree. Furthermore, it implemented Principal Components Analysis (PCA) along with Support Vector Machines (SVMs) algorithms for feature extraction and water quality degree classification. The datasets used for experiments were constructed based on real sample images for fish gills. Training dataset is divided into four classes representing the different histopathological changes and the corresponding water quality degrees. Experimental results showed that the proposed classification system has obtained water quality classification accuracy of 95.41%, using the SVMs linear kernel function and 10-fold cross validation with 37 images per class for training. (C) 2015 The Authors

Název v anglickém jazyce

Water Pollution Detection System Based on Fish Gills as a Biomarker

Popis výsledku anglicky

This article presents an automatic system for assessing water quality based on fish gills microscopic images. As fish gills are a good biomarker for assessing water quality, the proposed system uses fish gills microscopic images in order to detect water pollution. The proposed system consists of three phases; namely pre-processing, feature extraction, and classification phases. Since the shape is the main characteristic of fish gills microscopic images, the proposed system uses shape feature based on edge detection and wavelets transform for classifying the water-quality degree. Furthermore, it implemented Principal Components Analysis (PCA) along with Support Vector Machines (SVMs) algorithms for feature extraction and water quality degree classification. The datasets used for experiments were constructed based on real sample images for fish gills. Training dataset is divided into four classes representing the different histopathological changes and the corresponding water quality degrees. Experimental results showed that the proposed classification system has obtained water quality classification accuracy of 95.41%, using the SVMs linear kernel function and 10-fold cross validation with 37 images per class for training. (C) 2015 The Authors

Druh

D - Stať ve sborníku

CEP obor

IN - Informatika

OECD FORD obor

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Projekt

—

Návaznosti

S - Specificky vyzkum na vysokych skolach

Rok uplatnění

2015

Kód důvěrnosti údajů

S - Úplné a pravdivé údaje o projektu nepodléhají ochraně podle zvláštních právních předpisů

Název statě ve sborníku

Procedia Computer Science. Volume 65

ISBN

—

ISSN

1877-0509

e-ISSN

—

Počet stran výsledku

11

Strana od-do

601-611

Název nakladatele

Elsevier

Místo vydání

Amsterdam

Místo konání akce

Praha

Datum konání akce

20. 4. 2015

Typ akce podle státní příslušnosti

WRD - Celosvětová akce

Kód UT WoS článku

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