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A preliminary study on automated freshwater algae recognition and classification system

BACKGROUND: Freshwater algae can be used as indicators to monitor freshwater ecosystem condition. Algae react quickly and predictably to a broad range of pollutants. Thus they provide early signals of worsening environment. This study was carried out to develop a computer-based image processing tech...

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Autores principales: Mosleh, Mogeeb AA, Manssor, Hayat, Malek, Sorayya, Milow, Pozi, Salleh, Aishah
Formato: Online Artículo Texto
Lenguaje:English
Publicado: BioMed Central 2012
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC3521397/
https://www.ncbi.nlm.nih.gov/pubmed/23282059
http://dx.doi.org/10.1186/1471-2105-13-S17-S25
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author Mosleh, Mogeeb AA
Manssor, Hayat
Malek, Sorayya
Milow, Pozi
Salleh, Aishah
author_facet Mosleh, Mogeeb AA
Manssor, Hayat
Malek, Sorayya
Milow, Pozi
Salleh, Aishah
author_sort Mosleh, Mogeeb AA
collection PubMed
description BACKGROUND: Freshwater algae can be used as indicators to monitor freshwater ecosystem condition. Algae react quickly and predictably to a broad range of pollutants. Thus they provide early signals of worsening environment. This study was carried out to develop a computer-based image processing technique to automatically detect, recognize, and identify algae genera from the divisions Bacillariophyta, Chlorophyta and Cyanobacteria in Putrajaya Lake. Literature shows that most automated analyses and identification of algae images were limited to only one type of algae. Automated identification system for tropical freshwater algae is even non-existent and this study is partly to fill this gap. RESULTS: The development of the automated freshwater algae detection system involved image preprocessing, segmentation, feature extraction and classification by using Artificial neural networks (ANN). Image preprocessing was used to improve contrast and remove noise. Image segmentation using canny edge detection algorithm was then carried out on binary image to detect the algae and its boundaries. Feature extraction process was applied to extract specific feature parameters from algae image to obtain some shape and texture features of selected algae such as shape, area, perimeter, minor and major axes, and finally Fourier spectrum with principal component analysis (PCA) was applied to extract some of algae feature texture. Artificial neural network (ANN) is used to classify algae images based on the extracted features. Feed-forward multilayer perceptron network was initialized with back propagation error algorithm, and trained with extracted database features of algae image samples. System's accuracy rate was obtained by comparing the results between the manual and automated classifying methods. The developed system was able to identify 93 images of selected freshwater algae genera from a total of 100 tested images which yielded accuracy rate of 93%. CONCLUSIONS: This study demonstrated application of automated algae recognition of five genera of freshwater algae. The result indicated that MLP is sufficient, and can be used for classification of freshwater algae. However for future studies, application of support vector machine (SVM) and radial basis function (RBF) should be considered for better classifying as the number of algae species studied increases.
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spelling pubmed-35213972012-12-14 A preliminary study on automated freshwater algae recognition and classification system Mosleh, Mogeeb AA Manssor, Hayat Malek, Sorayya Milow, Pozi Salleh, Aishah BMC Bioinformatics Proceedings BACKGROUND: Freshwater algae can be used as indicators to monitor freshwater ecosystem condition. Algae react quickly and predictably to a broad range of pollutants. Thus they provide early signals of worsening environment. This study was carried out to develop a computer-based image processing technique to automatically detect, recognize, and identify algae genera from the divisions Bacillariophyta, Chlorophyta and Cyanobacteria in Putrajaya Lake. Literature shows that most automated analyses and identification of algae images were limited to only one type of algae. Automated identification system for tropical freshwater algae is even non-existent and this study is partly to fill this gap. RESULTS: The development of the automated freshwater algae detection system involved image preprocessing, segmentation, feature extraction and classification by using Artificial neural networks (ANN). Image preprocessing was used to improve contrast and remove noise. Image segmentation using canny edge detection algorithm was then carried out on binary image to detect the algae and its boundaries. Feature extraction process was applied to extract specific feature parameters from algae image to obtain some shape and texture features of selected algae such as shape, area, perimeter, minor and major axes, and finally Fourier spectrum with principal component analysis (PCA) was applied to extract some of algae feature texture. Artificial neural network (ANN) is used to classify algae images based on the extracted features. Feed-forward multilayer perceptron network was initialized with back propagation error algorithm, and trained with extracted database features of algae image samples. System's accuracy rate was obtained by comparing the results between the manual and automated classifying methods. The developed system was able to identify 93 images of selected freshwater algae genera from a total of 100 tested images which yielded accuracy rate of 93%. CONCLUSIONS: This study demonstrated application of automated algae recognition of five genera of freshwater algae. The result indicated that MLP is sufficient, and can be used for classification of freshwater algae. However for future studies, application of support vector machine (SVM) and radial basis function (RBF) should be considered for better classifying as the number of algae species studied increases. BioMed Central 2012-12-07 /pmc/articles/PMC3521397/ /pubmed/23282059 http://dx.doi.org/10.1186/1471-2105-13-S17-S25 Text en Copyright ©2012 Mosleh et al.; licensee BioMed Central Ltd. http://creativecommons.org/licenses/by/2.0 This is an open access article distributed under the terms of the Creative Commons Attribution License (http://creativecommons.org/licenses/by/2.0), which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
spellingShingle Proceedings
Mosleh, Mogeeb AA
Manssor, Hayat
Malek, Sorayya
Milow, Pozi
Salleh, Aishah
A preliminary study on automated freshwater algae recognition and classification system
title A preliminary study on automated freshwater algae recognition and classification system
title_full A preliminary study on automated freshwater algae recognition and classification system
title_fullStr A preliminary study on automated freshwater algae recognition and classification system
title_full_unstemmed A preliminary study on automated freshwater algae recognition and classification system
title_short A preliminary study on automated freshwater algae recognition and classification system
title_sort preliminary study on automated freshwater algae recognition and classification system
topic Proceedings
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC3521397/
https://www.ncbi.nlm.nih.gov/pubmed/23282059
http://dx.doi.org/10.1186/1471-2105-13-S17-S25
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