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Sonmez ME, Gumus NE, Eczacioglu N, Develi EE, Yücel K, Yildiz HB. Enhancing microalgae classification accuracy in marine ecosystems through convolutional neural networks and support vector machines. MARINE POLLUTION BULLETIN 2024; 205:116616. [PMID: 38936001 DOI: 10.1016/j.marpolbul.2024.116616] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 03/13/2024] [Revised: 06/13/2024] [Accepted: 06/16/2024] [Indexed: 06/29/2024]
Abstract
Accurately classifying microalgae species is vital for monitoring marine ecosystems and managing the emergence of marine mucilage, which is crucial for monitoring mucilage phenomena in marine environments. Traditional methods have been inadequate due to time-consuming processes and the need for expert knowledge. The purpose of this article is to employ convolutional neural networks (CNNs) and support vector machines (SVMs) to improve classification accuracy and efficiency. By employing advanced computational techniques, including MobileNet and GoogleNet models, alongside SVM classification, the study demonstrates significant advancements over conventional identification methods. In the classification of a dataset consisting of 7820 images using four different SVM kernel functions, the linear kernel achieved the highest success rate at 98.79 %. It is followed by the RBF kernel at 98.73 %, the polynomial kernel at 97.84 %, and the sigmoid kernel at 97.20 %. This research not only provides a methodological framework for future studies in marine biodiversity monitoring but also highlights the potential for real-time applications in ecological conservation and understanding mucilage dynamics amidst climate change and environmental pollution.
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Affiliation(s)
- Mesut Ersin Sonmez
- Department of Bioengineering, Faculty of Engineering, Karamanoglu Mehmetbey University, Karaman, Türkiye
| | - Numan Emre Gumus
- Department of Environmental Protection Technology, Kazım Karabekir Vocational School, Karamanoglu Mehmetbey University, Karaman, Türkiye.
| | - Numan Eczacioglu
- Department of Bioengineering, Faculty of Engineering, Karamanoglu Mehmetbey University, Karaman, Türkiye
| | | | - Kamile Yücel
- Department of Medical Biochemistry, Faculty of Medicine, KTO, Karatay University, Konya, Türkiye
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2
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Jia R, Yin G, Zhao N, Chen X, Xu M, Hu X, Huang P, Liang T, He Q, Zhang X. Phytoplankton Image Segmentation and Annotation Method Based on Microscopic Fluorescence. J Fluoresc 2023:10.1007/s10895-023-03515-6. [PMID: 38055139 DOI: 10.1007/s10895-023-03515-6] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/24/2023] [Accepted: 11/15/2023] [Indexed: 12/07/2023]
Abstract
Microscopic phytoplankton segmentation is an important part of water quality assessment. The segmentation of microscopic phytoplankton still faces challenges for computer vision, such as being affected by background impurities and requiring a large number of manual annotation. In this paper, the characteristics of phytoplankton emitting fluorescence under excitation light were utilized to segment and annotate phytoplankton contours by fusing fluorescence images and bright field images. Morphological operations were used to process microscopic fluorescence images to obtain the initial contours of phytoplankton. Then, microscopic bright field images were processed by Active Contour to fine tune the contours. Seven algae species were selected as the experimental objects. Compared with manually labeling the contour in LabelMe, the recall, precision, FI score and IOU of the proposed segmentation method are 85.3%, 84.5%, 84.7%, and 74.6%, respectively. Mask-RCNN was used to verify the correctness of labels annotated by the proposed method. The average recall, precision, F1 score and IOU are 97.0%, 86.5%, 91.1%, and 84.2%, respectively, when the Mask-RCNN is trained with the proposed automatic labeling method. And the results corresponding to manual labeling are 95.3%, 86.1%, 90.3%, and 82.8% respectively. The experimental results show that the proposed method can segment the phytoplankton microscopic image accurately, and the automatically annotated contour data has the same effect as the manually annotated contour data in Mask-RCNN, which greatly reduces the manual annotation workload.
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Affiliation(s)
- Renqing Jia
- Key Laboratory of Environment Optics and Technology, Anhui Institute of Optics and Fine Mechanics, Institutes of Physical Science, Chinese Academy of Sciences, Hefei, 230031, China
| | - Gaofang Yin
- Key Laboratory of Environment Optics and Technology, Anhui Institute of Optics and Fine Mechanics, Institutes of Physical Science, Chinese Academy of Sciences, Hefei, 230031, China.
| | - Nanjing Zhao
- Key Laboratory of Environment Optics and Technology, Anhui Institute of Optics and Fine Mechanics, Institutes of Physical Science, Chinese Academy of Sciences, Hefei, 230031, China.
| | - Xiaowei Chen
- Key Laboratory of Environment Optics and Technology, Anhui Institute of Optics and Fine Mechanics, Institutes of Physical Science, Chinese Academy of Sciences, Hefei, 230031, China
| | - Min Xu
- Key Laboratory of Environment Optics and Technology, Anhui Institute of Optics and Fine Mechanics, Institutes of Physical Science, Chinese Academy of Sciences, Hefei, 230031, China
| | - Xiang Hu
- Key Laboratory of Environment Optics and Technology, Anhui Institute of Optics and Fine Mechanics, Institutes of Physical Science, Chinese Academy of Sciences, Hefei, 230031, China
| | - Peng Huang
- Key Laboratory of Environment Optics and Technology, Anhui Institute of Optics and Fine Mechanics, Institutes of Physical Science, Chinese Academy of Sciences, Hefei, 230031, China
| | - Tianhong Liang
- Key Laboratory of Environment Optics and Technology, Anhui Institute of Optics and Fine Mechanics, Institutes of Physical Science, Chinese Academy of Sciences, Hefei, 230031, China
| | - Qianfeng He
- Anhui Hefei Ecological Environment Monitoring Center, Hefei, 230088, China
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ALAKUŞ TB. Classification of Microbes with Recurrent Neural Networks. FIRAT ÜNIVERSITESI MÜHENDISLIK BILIMLERI DERGISI 2023. [DOI: 10.35234/fumbd.1302903] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 09/01/2023]
Abstract
Mikroplar insan vücudunun hem içinde hem de dışında yaşayan ve hemen hemen her yerde bulunan tek hücreli küçük canlılardır. Zararlı olabileceği gibi zararı olmayan mikroplarda bulunmaktadır. Bu yüzden mikroplar ekolojik dengenin sağlanmasında büyük bir rol üstlenmektedir. Ancak, bu mikropları birbirinden ayırt edebilmek çoğu zaman meşakkatli bir iş olmaktadır. Mikropların çeşitli özelliklerini anlayan ve taksonomi nüanslarını yorumlayabilen bir uzman bilgisinin gerekmesi ve laboratuvar ihtiyacının olması bu süreci hem maliyetli hem de zaman alıcı yapmaktadır. Bundan dolayı, bilgisayar destekli sistemlerin bu alanda önemi artmış ve günümüzde yapay zeka teknolojileri mikropların sınıflandırılmasında kullanılmaya başlanmıştır. Makine öğrenmesi ve derin öğrenme teknolojilerinin bu alana uygulanmasıyla yüksek seviyede doğruluk skorları elde edilmekte ve biyoçeşitlilikle ilgili değerlendirmeler otomatik bir şekilde yapılabilmektedir. Bu çalışmada da derin öğrenme modellerinden biri olan tekrarlayıcı sinir ağları yapıları kullanılarak, mikropların sınıflandırılması yapılmıştır. Çalışma dört aşamadan meydana gelmiştir. Birinci aşamada, veriler elde edilmiştir. İkinci aşamada bu veriler normalleştirme işlemine tabi tutulmuş ve önişlemden geçirilmiştir. Üçüncü aşamada UKSB ve TSA modelleri tasarlanmış ve sınıflandırılmıştır. Son aşamada ise sınıflandırıcıların performansları doğruluk, kesinlik, duyarlılık, F1-skor ve AUC skoru ile belirlenmiştir. Çalışmanın sonunda TSA sınıflandırıcısı ile %92.53, UKSB sınıflandırıcısıyla ise %99.85 doğruluk skoru elde edilmiştir.
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4
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Chong JWR, Khoo KS, Chew KW, Ting HY, Show PL. Trends in digital image processing of isolated microalgae by incorporating classification algorithm. Biotechnol Adv 2023; 63:108095. [PMID: 36608745 DOI: 10.1016/j.biotechadv.2023.108095] [Citation(s) in RCA: 7] [Impact Index Per Article: 7.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/23/2022] [Revised: 12/17/2022] [Accepted: 01/01/2023] [Indexed: 01/05/2023]
Abstract
Identification of microalgae species is of importance due to the uprising of harmful algae blooms affecting both the aquatic habitat and human health. Despite this occurence, microalgae have been identified as a green biomass and alternative source due to its promising bioactive compounds accumulation that play a significant role in many industrial applications. Recently, microalgae species identification has been conducted through DNA analysis and various microscopy techniques such as light, scanning electron, transmission electron, and atomic force -microscopy. The aforementioned procedures have encouraged researchers to consider alternate ways due to limitations such as costly validation, requiring skilled taxonomists, prolonged analysis, and low accuracy. This review highlights the potential innovations in digital microscopy with the incorporation of both hardware and software that can produce a reliable recognition, detection, enumeration, and real-time acquisition of microalgae species. Several steps such as image acquisition, processing, feature extraction, and selection are discussed, for the purpose of generating high image quality by removing unwanted artifacts and noise from the background. These steps of identification of microalgae species is performed by reliable image classification through machine learning as well as deep learning algorithms such as artificial neural networks, support vector machines, and convolutional neural networks. Overall, this review provides comprehensive insights into numerous possibilities of microalgae image identification, image pre-processing, and machine learning techniques to address the challenges in developing a robust digital classification tool for the future.
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Affiliation(s)
- Jun Wei Roy Chong
- Zhejiang Provincial Key Laboratory for Subtropical Water Environment and Marine Biological Resources Protection, Wenzhou University, Wenzhou 325035, China; Department of Chemical and Environmental Engineering, Faculty of Science and Engineering, University of Nottingham Malaysia, Jalan Broga, Semenyih 43500, Selangor Darul Ehsan, Malaysia
| | - Kuan Shiong Khoo
- Department of Chemical Engineering and Materials Science, Yuan Ze University, Taoyuan, Taiwan.
| | - Kit Wayne Chew
- School of Chemistry, Chemical Engineering and Biotechnology, Nanyang Technological University, 62 Nanyang Drive, Singapore, 637459 Singapore
| | - Huong-Yong Ting
- Drone Research and Application Centre, University of Technology Sarawak, No.1, Jalan Universiti, 96000 Sibu, Sarawak, Malaysia
| | - Pau Loke Show
- Zhejiang Provincial Key Laboratory for Subtropical Water Environment and Marine Biological Resources Protection, Wenzhou University, Wenzhou 325035, China; Department of Chemical and Environmental Engineering, Faculty of Science and Engineering, University of Nottingham Malaysia, Jalan Broga, Semenyih 43500, Selangor Darul Ehsan, Malaysia; Department of Sustainable Engineering, Saveetha School of Engineering, SIMATS, Chennai 602105, India.
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5
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Krause LMK, Manderfeld E, Gnutt P, Vogler L, Wassick A, Richard K, Rudolph M, Hunsucker KZ, Swain GW, Rosenhahn B, Rosenhahn A. Semantic segmentation for fully automated macrofouling analysis on coatings after field exposure. BIOFOULING 2023; 39:64-79. [PMID: 36924139 DOI: 10.1080/08927014.2023.2185143] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 09/23/2022] [Revised: 02/16/2023] [Accepted: 02/20/2023] [Indexed: 06/18/2023]
Abstract
Biofouling is a major challenge for sustainable shipping, filter membranes, heat exchangers, and medical devices. The development of fouling-resistant coatings requires the evaluation of their effectiveness. Such an evaluation is usually based on the assessment of fouling progression after different exposure times to the target medium (e.g. salt water). The manual assessment of macrofouling requires expert knowledge about local fouling communities due to high variances in phenotypical appearance, has single-image sampling inaccuracies for certain species, and lacks spatial information. Here an approach for automatic image-based macrofouling analysis was presented. A dataset with dense labels prepared from field panel images was made and a convolutional network (adapted U-Net) for the semantic segmentation of different macrofouling classes was proposed. The establishment of macrofouling localization allows for the generation of a successional model which enables the determination of direct surface attachment and in-depth epibiotic studies.
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Affiliation(s)
- Lutz M K Krause
- Analytical Chemistry - Biointerfaces, Ruhr University Bochum, Bochum, Germany
| | - Emily Manderfeld
- Analytical Chemistry - Biointerfaces, Ruhr University Bochum, Bochum, Germany
| | - Patricia Gnutt
- Analytical Chemistry - Biointerfaces, Ruhr University Bochum, Bochum, Germany
| | - Louisa Vogler
- Analytical Chemistry - Biointerfaces, Ruhr University Bochum, Bochum, Germany
| | - Ann Wassick
- Center for Corrosion and Biofouling Control, Florida Institute of Technology, Melbourne, Florida, USA
| | - Kailey Richard
- Center for Corrosion and Biofouling Control, Florida Institute of Technology, Melbourne, Florida, USA
| | - Marco Rudolph
- Institute for Information Processing, Leibniz University Hannover, Hannover, Germany
| | - Kelli Z Hunsucker
- Center for Corrosion and Biofouling Control, Florida Institute of Technology, Melbourne, Florida, USA
| | - Geoffrey W Swain
- Center for Corrosion and Biofouling Control, Florida Institute of Technology, Melbourne, Florida, USA
| | - Bodo Rosenhahn
- Institute for Information Processing, Leibniz University Hannover, Hannover, Germany
| | - Axel Rosenhahn
- Analytical Chemistry - Biointerfaces, Ruhr University Bochum, Bochum, Germany
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6
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Novel Approach to Freshwater Diatom Profiling and Identification Using Raman Spectroscopy and Chemometric Analysis. WATER 2022. [DOI: 10.3390/w14132116] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 12/15/2022]
Abstract
(1) An approach with great potential for fast and cost-effective profiling and identification of diatoms in lake ecosystems is presented herein. This approach takes advantage of Raman spectroscopy. (2) The study was based on the analysis of 790 Raman spectra from 29 species, belonging to 15 genera, 12 families, 9 orders and 4 subclasses, which were analysed using chemometric methods. The Raman data were first analysed by a partial least squares regression discriminant analysis (PLS-DA) to characterise the diatom species. Furthermore, a method was developed to streamline the integrated interpretation of PLS-DA when a high number of significant components is extracted. Subsequently, an artificial neural network (ANN) was used for taxa identification from Raman data. (3) The PLS interpretation produced a Raman profile for each species reflecting its biochemical composition. The ANN models were useful to identify various taxa with high accuracy. (4) Compared to studies in the literature, involving huge datasets one to four orders of magnitude larger than ours, high sensitivity was found for the identification of Achnanthidium exiguum (67%), Fragilaria pararumpens (67%), Amphora pediculus (71%), Achnanthidium minutissimum (80%) and Melosira varians (82%).
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7
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Machine Learning Powered Microalgae Classification by Use of Polarized Light Scattering Data. APPLIED SCIENCES-BASEL 2022. [DOI: 10.3390/app12073422] [Citation(s) in RCA: 2] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 02/07/2023]
Abstract
Microalgae are widely distributed in the ocean, which greatly affects the ocean environment. In this work, a dataset is presented, including the polarized light scattering data of 35 categories of marine microalgae. To analyze the dataset, several machine learning algorithms are applied and compared, such as linear discrimination analysis (LDA) and two types of support vector machine (SVM). Results show that non-linear SVM performs the best among these algorithms. Then, two data preparation approaches for non-linear SVM are compared. Subsequently, more than 10 categories of microalgae out of the dataset can be identified with an accuracy greater than 0.80. The basis of the dataset is shown by finding the categories independent to each other. The discussions about the performance of different incident polarization of light gives some clues to design the optimal incident polarization of light for future instrumentation. With this proposed technique and the dataset, these microalgae can be well differentiated by polarized light scattering data.
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8
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Rani P, Kotwal S, Manhas J, Sharma V, Sharma S. Machine Learning and Deep Learning Based Computational Approaches in Automatic Microorganisms Image Recognition: Methodologies, Challenges, and Developments. ARCHIVES OF COMPUTATIONAL METHODS IN ENGINEERING : STATE OF THE ART REVIEWS 2022; 29:1801-1837. [PMID: 34483651 PMCID: PMC8405717 DOI: 10.1007/s11831-021-09639-x] [Citation(s) in RCA: 12] [Impact Index Per Article: 6.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Received: 04/27/2021] [Accepted: 08/24/2021] [Indexed: 05/12/2023]
Abstract
Microorganisms or microbes comprise majority of the diversity on earth and are extremely important to human life. They are also integral to processes in the ecosystem. The process of their recognition is highly tedious, but very much essential in microbiology to carry out different experimentation. To overcome certain challenges, machine learning techniques assist microbiologists in automating the entire process. This paper presents a systematic review of research done using machine learning (ML) and deep leaning techniques in image recognition of different microorganisms. This review investigates certain research questions to analyze the studies concerning image pre-processing, feature extraction, classification techniques, evaluation measures, methodological limitations and technical development over a period of time. In addition to this, this paper also addresses the certain challenges faced by researchers in this field. Total of 100 research publications in the chronological order of their appearance have been considered for the time period 1995-2021. This review will be extremely beneficial to the researchers due to the detailed analysis of different methodologies and comprehensive overview of effectiveness of different ML techniques being applied in microorganism image recognition field.
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Affiliation(s)
- Priya Rani
- Computer Science and IT, University of Jammu, Jammu, India
| | - Shallu Kotwal
- Information Technology, Baba Ghulam Shah Badshah University, Rajouri, India
| | - Jatinder Manhas
- Computer Science and IT, Bhaderwah Campus, University of Jammu, Jammu, India
| | - Vinod Sharma
- Computer Science and IT, University of Jammu, Jammu, India
| | - Sparsh Sharma
- Department of Computer Science and Engineering, NIT Srinagar, Srinagar, J&K India
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9
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Dhindsa A, Bhatia S, Agrawal S, Sohi BS. An Improvised Machine Learning Model Based on Mutual Information Feature Selection Approach for Microbes Classification. ENTROPY (BASEL, SWITZERLAND) 2021; 23:257. [PMID: 33672252 PMCID: PMC7927045 DOI: 10.3390/e23020257] [Citation(s) in RCA: 5] [Impact Index Per Article: 1.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 01/14/2021] [Revised: 02/10/2021] [Accepted: 02/20/2021] [Indexed: 12/11/2022]
Abstract
The accurate classification of microbes is critical in today's context for monitoring the ecological balance of a habitat. Hence, in this research work, a novel method to automate the process of identifying microorganisms has been implemented. To extract the bodies of microorganisms accurately, a generalized segmentation mechanism which consists of a combination of convolution filter (Kirsch) and a variance-based pixel clustering algorithm (Otsu) is proposed. With exhaustive corroboration, a set of twenty-five features were identified to map the characteristics and morphology for all kinds of microbes. Multiple techniques for feature selection were tested and it was found that mutual information (MI)-based models gave the best performance. Exhaustive hyperparameter tuning of multilayer layer perceptron (MLP), k-nearest neighbors (KNN), quadratic discriminant analysis (QDA), logistic regression (LR), and support vector machine (SVM) was done. It was found that SVM radial required further improvisation to attain a maximum possible level of accuracy. Comparative analysis between SVM and improvised SVM (ISVM) through a 10-fold cross validation method ultimately showed that ISVM resulted in a 2% higher performance in terms of accuracy (98.2%), precision (98.2%), recall (98.1%), and F1 score (98.1%).
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Affiliation(s)
- Anaahat Dhindsa
- Department of Electronics and Communication Engineering, Chandigarh University, Gharuan, Punjab 140413, India;
- University Institute of Engineering and Technology, Panjab University, Chandigarh 160014, India;
| | - Sanjay Bhatia
- Post Graduate Department of Zoology, University of Jammu, Kashmir 180006, India;
| | - Sunil Agrawal
- University Institute of Engineering and Technology, Panjab University, Chandigarh 160014, India;
| | - Balwinder Singh Sohi
- Department of Electronics and Communication Engineering, Chandigarh University, Gharuan, Punjab 140413, India;
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10
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Krause LMK, Koc J, Rosenhahn B, Rosenhahn A. Fully Convolutional Neural Network for Detection and Counting of Diatoms on Coatings after Short-Term Field Exposure. ENVIRONMENTAL SCIENCE & TECHNOLOGY 2020; 54:10022-10030. [PMID: 32663392 DOI: 10.1021/acs.est.0c01982] [Citation(s) in RCA: 10] [Impact Index Per Article: 2.5] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 06/11/2023]
Abstract
While the use of deep learning is a valuable technology for automatic detection systems for medical data and images, the biofouling community is still lacking an analytical tool for the detection and counting of diatoms on samples after short-term field exposure. In this work, a fully convolutional neural network was implemented as a fast and simple approach to detect diatoms on two-channel (fluorescence and phase-contrast) microscopy images by predicting bounding boxes. The developed approach performs well with only a small number of trainable parameters and a F1 score of 0.82. Counting diatoms was evaluated on a data set of 600 microscopy images of three different surface chemistries (hydrophilic and hydrophobic) and is very similar to counting by humans while demanding only a fraction of the analysis time.
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Affiliation(s)
- Lutz M K Krause
- Analytical Chemistry-Biointerfaces, Ruhr University Bochum, 44801 Bochum, Germany
| | - Julian Koc
- Analytical Chemistry-Biointerfaces, Ruhr University Bochum, 44801 Bochum, Germany
| | - Bodo Rosenhahn
- Institute for Information Processing, Leibniz University Hannover, 30167 Hannover, Germany
| | - Axel Rosenhahn
- Analytical Chemistry-Biointerfaces, Ruhr University Bochum, 44801 Bochum, Germany
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11
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Investigating the Automatic Classification of Algae Using the Spectral and Morphological Characteristics via Deep Residual Learning. LECTURE NOTES IN COMPUTER SCIENCE 2019. [DOI: 10.1007/978-3-030-27272-2_23] [Citation(s) in RCA: 7] [Impact Index Per Article: 1.4] [Reference Citation Analysis] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 12/13/2022]
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12
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Kho SJ, Manickam S, Malek S, Mosleh M, Dhillon SK. Automated plant identification using artificial neural network and support vector machine. FRONTIERS IN LIFE SCIENCE 2018. [DOI: 10.1080/21553769.2017.1412361] [Citation(s) in RCA: 12] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 10/18/2022]
Affiliation(s)
- Soon Jye Kho
- Faculty of Science, Data Science & Bioinformatics Laboratory, Institute of Biological Sciences, University of Malaya, Kuala Lumpur, Malaysia
| | - Sugumaran Manickam
- Faculty of Science, Rimba Ilmu Botanic Garden, Institute of Biological Sciences, University of Malaya, Kuala Lumpur, Malaysia
| | - Sorayya Malek
- Faculty of Science, Data Science & Bioinformatics Laboratory, Institute of Biological Sciences, University of Malaya, Kuala Lumpur, Malaysia
| | - Mogeeb Mosleh
- Faculty of Engineering & Information Technology, Software Engineering Department, Taiz University, Taiz, Yemen
| | - Sarinder Kaur Dhillon
- Faculty of Science, Data Science & Bioinformatics Laboratory, Institute of Biological Sciences, University of Malaya, Kuala Lumpur, Malaysia
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13
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A survey for the applications of content-based microscopic image analysis in microorganism classification domains. Artif Intell Rev 2017. [DOI: 10.1007/s10462-017-9572-4] [Citation(s) in RCA: 40] [Impact Index Per Article: 5.7] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/16/2023]
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14
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A Multi-Platform Optical Sensor for In Vivo and In Vitro Algae Classification. SENSORS 2017; 17:s17040912. [PMID: 28425963 PMCID: PMC5426836 DOI: 10.3390/s17040912] [Citation(s) in RCA: 6] [Impact Index Per Article: 0.9] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 03/24/2017] [Revised: 04/14/2017] [Accepted: 04/19/2017] [Indexed: 12/04/2022]
Abstract
Differentiation among major algal groups is important for the ecological and biogeochemical characterization of water bodies, and for practical management of water resources. It helps to discern the taxonomic groups that are beneficial to aquatic life from the organisms causing harmful algal blooms. An LED-induced fluorescence (LEDIF) instrument capable of fluorescence, absorbance, and scattering measurements; is used for in vivo and in vitro identification and quantification of four algal groups found in freshwater and marine environments. Aqueous solutions of individual and mixed dissolved biological pigments relevant to different algal groups were measured to demonstrate the LEDIF’s capabilities in measuring extracted pigments. Different genera of algae were cultivated and the cell counts of the samples were quantified with a hemacytometer and/or cellometer. Dry weight of different algae cells was also measured to determine the cell counts-to-dry weight correlations. Finally, in vivo measurements of different genus of algae at different cell concentrations and mixed algal group in the presence of humic acid were performed with the LEDIF. A field sample from a local reservoir was measured with the LEDIF and the results were verified using hemacytometer, cellometer, and microscope. The results demonstrated the LEDIF’s capabilities in classifying and quantifying different groups of live algae.
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15
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Coltelli P, Barsanti L, Evangelista V, Gualtieri P. Algae through the looking glass. Microsc Res Tech 2017; 80:486-494. [DOI: 10.1002/jemt.22820] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/10/2016] [Accepted: 11/30/2016] [Indexed: 11/06/2022]
Affiliation(s)
- Primo Coltelli
- Istituto Scienza e Tecnologie dell'Informazione, CNR, Via Moruzzi 1; Pisa 56124 Italy
| | - Laura Barsanti
- Istituto di Biofisica, CNR, Via Moruzzi 1; Pisa 56124 Italy
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16
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Yousef Kalafi E, Tan WB, Town C, Dhillon SK. Automated identification of Monogeneans using digital image processing and K-nearest neighbour approaches. BMC Bioinformatics 2016; 17:511. [PMID: 28155722 PMCID: PMC5260054 DOI: 10.1186/s12859-016-1376-z] [Citation(s) in RCA: 13] [Impact Index Per Article: 1.6] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/10/2022] Open
Abstract
BACKGROUND Monogeneans are flatworms (Platyhelminthes) that are primarily found on gills and skin of fishes. Monogenean parasites have attachment appendages at their haptoral regions that help them to move about the body surface and feed on skin and gill debris. Haptoral attachment organs consist of sclerotized hard parts such as hooks, anchors and marginal hooks. Monogenean species are differentiated based on their haptoral bars, anchors, marginal hooks, reproductive parts' (male and female copulatory organs) morphological characters and soft anatomical parts. The complex structure of these diagnostic organs and also their overlapping in microscopic digital images are impediments for developing fully automated identification system for monogeneans (LNCS 7666:256-263, 2012), (ISDA; 457-462, 2011), (J Zoolog Syst Evol Res 52(2): 95-99. 2013;). In this study images of hard parts of the haptoral organs such as bars and anchors are used to develop a fully automated identification technique for monogenean species identification by implementing image processing techniques and machine learning methods. RESULT Images of four monogenean species namely Sinodiplectanotrema malayanus, Trianchoratus pahangensis, Metahaliotrema mizellei and Metahaliotrema sp. (undescribed) were used to develop an automated technique for identification. K-nearest neighbour (KNN) was applied to classify the monogenean specimens based on the extracted features. 50% of the dataset was used for training and the other 50% was used as testing for system evaluation. Our approach demonstrated overall classification accuracy of 90%. In this study Leave One Out (LOO) cross validation is used for validation of our system and the accuracy is 91.25%. CONCLUSIONS The methods presented in this study facilitate fast and accurate fully automated classification of monogeneans at the species level. In future studies more classes will be included in the model, the time to capture the monogenean images will be reduced and improvements in extraction and selection of features will be implemented.
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Affiliation(s)
- Elham Yousef Kalafi
- Institute of Biological Sciences, Faculty of Science, University of Malaya, Kuala Lumpur, Malaysia
| | - Wooi Boon Tan
- Institute of Biological Sciences, Faculty of Science, University of Malaya, Kuala Lumpur, Malaysia
| | - Christopher Town
- Computer Laboratory, University of Cambridge, Cambridge, CB3 0FD, UK
| | - Sarinder Kaur Dhillon
- Institute of Biological Sciences, Faculty of Science, University of Malaya, Kuala Lumpur, Malaysia. .,Computer Laboratory, University of Cambridge, Cambridge, CB3 0FD, UK.
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Barsanti L, Coltelli P, Evangelista V, Frassanito AM, Gualtieri P. Swimming patterns of the quadriflagellate Tetraflagellochloris mauritanica (Chlamydomonadales, Chlorophyceae). JOURNAL OF PHYCOLOGY 2016; 52:209-218. [PMID: 27037586 DOI: 10.1111/jpy.12384] [Citation(s) in RCA: 5] [Impact Index Per Article: 0.6] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 07/09/2015] [Accepted: 12/04/2015] [Indexed: 06/05/2023]
Abstract
Chlamydomonadales are elective subjects for the investigation of the problems related to locomotion and transport in biological fluid dynamics, whose resolution could enhance searching efficiency and assist in the avoidance of dangerous environments. In this paper, we elucidate the swimming behavior of Tetraflagellochloris mauritanica, a unicellular-multicellular alga belonging to the order Chlamydomonadales. This quadriflagellate alga has a complex swimming motion consisting of alternating swimming phases connected by in-place random reorientations and resting phases. It is capable of both forward and backward swimming, both being normal modes of swimming. The complex swimming behavior resembles the run-and-tumble motion of peritrichous bacteria, with in-place reorientation taking the place of tumbles. In the forward swimming, T. mauritanica shows a very efficient flagellar beat, with undulatory retrograde waves that run along the flagella to their tip. In the backward swimming, the flagella show a nonstereotypical synchronization mode, with a pattern that does not fit any of the modes present in the other Chlamydomonadales so far investigated.
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Affiliation(s)
- Laura Barsanti
- Istituto di Biofisica, CNR, Via Moruzzi 1, Pisa, 56124, Italy
| | - Primo Coltelli
- Istituto di Scienza e Tecnologia Informazione, CNR, Via Moruzzi 1, Pisa, 56124, Italy
| | | | | | - Paolo Gualtieri
- Istituto di Biofisica, CNR, Via Moruzzi 1, Pisa, 56124, Italy
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Schönbach C, Horton P, Yiu SM, Tan TW, Ranganathan S. GIW and InCoB are advancing bioinformatics in the Asia-Pacific. BMC Bioinformatics 2015; 16:I1. [PMID: 28102114 PMCID: PMC6389036 DOI: 10.1186/1471-2105-16-s18-i1] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/14/2022] Open
Abstract
GIW/InCoB2015 the joint 26th International Conference on Genome Informatics (GIW) and 14th International Conference on Bioinformatics (InCoB) held in Tokyo, September 9-11, 2015 was attended by over 200 delegates. Fifty-one out of 89 oral presentations were based on research articles accepted for publication in four BMC journal supplements and three other journals. Sixteen articles in this supplement and six articles in the BMC Systems Biology GIW/InCoB2015 Supplement are covered by this introduction. The topics range from genome informatics, protein structure informatics, image analysis to biological networks and biomarker discovery.
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Affiliation(s)
- Christian Schönbach
- Department of Biology, School of Science and Technology, Nazarbayev University, Astana, 010000 Republic of Kazakhstan
- Center for AIDS Research and International Research Center for Medical Sciences, Kumamoto University, Kumamoto, 860-0811 Japan
| | - Paul Horton
- Computational Biology Research Center, National Institute of Advanced Industrial Science and Technology, Tokyo, 135-0064 Japan
- Department of Computational Biology, Graduate School of Frontier Sciences, University of Tokyo, Japan
| | - Siu-Ming Yiu
- Department of Computer Science, Faculty of Engineering, The University of Hong Kong, Hong Kong, HKSAR
| | - Tin Wee Tan
- Department of Biochemistry, Yong Loo Lin School of Medicine, National University of Singapore, Singapore, 117599
| | - Shoba Ranganathan
- Department of Biochemistry, Yong Loo Lin School of Medicine, National University of Singapore, Singapore, 117599
- Department of Chemistry and Biomolecular Sciences, Macquarie University, Sydney, NSW 2109 Australia
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Leow LK, Chew LL, Chong VC, Dhillon SK. Automated identification of copepods using digital image processing and artificial neural network. BMC Bioinformatics 2015; 16 Suppl 18:S4. [PMID: 26678287 PMCID: PMC4682403 DOI: 10.1186/1471-2105-16-s18-s4] [Citation(s) in RCA: 20] [Impact Index Per Article: 2.2] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/10/2022] Open
Abstract
Background Copepods are planktonic organisms that play a major role in the marine food chain. Studying the community structure and abundance of copepods in relation to the environment is essential to evaluate their contribution to mangrove trophodynamics and coastal fisheries. The routine identification of copepods can be very technical, requiring taxonomic expertise, experience and much effort which can be very time-consuming. Hence, there is an urgent need to introduce novel methods and approaches to automate identification and classification of copepod specimens. This study aims to apply digital image processing and machine learning methods to build an automated identification and classification technique. Results We developed an automated technique to extract morphological features of copepods' specimen from captured images using digital image processing techniques. An Artificial Neural Network (ANN) was used to classify the copepod specimens from species Acartia spinicauda, Bestiolina similis, Oithona aruensis, Oithona dissimilis, Oithona simplex, Parvocalanus crassirostris, Tortanus barbatus and Tortanus forcipatus based on the extracted features. 60% of the dataset was used for a two-layer feed-forward network training and the remaining 40% was used as testing dataset for system evaluation. Our approach demonstrated an overall classification accuracy of 93.13% (100% for A. spinicauda, B. similis and O. aruensis, 95% for T. barbatus, 90% for O. dissimilis and P. crassirostris, 85% for O. similis and T. forcipatus). Conclusions The methods presented in this study enable fast classification of copepods to the species level. Future studies should include more classes in the model, improving the selection of features, and reducing the time to capture the copepod images.
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