Special Issue "Automated Analysis and Identification of Phytoplankton Images"

A special issue of Applied Sciences (ISSN 2076-3417).

Deadline for manuscript submissions: closed (28 February 2017)

Special Issue Editors

Guest Editor
Dr. Gabriel Cristobal

Instituto de Óptica, Spanish National Research Council (CSIC), Serrano 121, 28006, Madrid, Spain
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Guest Editor
Dr. Saúl Blanco Lanza

Department of Biodiversity and Environmental Management University of León, La Serna 58, 24007 León, Spain
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Guest Editor
Dr. Gloria Bueno

ETS Ingenieros Industriales VISLAB Grupo de Vision y Sistemas Inteligentes Av. Camilo José Cela, s/n, 13071 Ciudad Real, Spain
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Guest Editor
Prof. Jan Kybic

Department of Cybernetics, Czech Technical University, Prague, Czech Republic
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Special Issue Information

Dear Colleagues,

High resolution images of phytoplankton cells, such as diatoms or desmids, can now be acquired by digital microscopes, which facilitates automating the analysis and identification process of specimens. These algae are useful for monitoring water quality, hence the importance of automating the analysis procedure. The conventional approach has usually consisted of identification and quantification using optical microscopy; however, there is a need for automated recognition techniques for diagnostic tools (networks of environmental monitoring, early warning systems) to facilitate proper management of water resources and decision-making processes. However, manual image analysis of such systems is impractical due to the huge diversity of this group of microalgae and its great morphological plasticity. New image analysis systems offer a potentially advantageous compared to manual methods of counting and identification solution. This Special Issue draws attention for contributions that will help to cover the entire workflow of a bioindicator system from capture, analysis or identification to the determination of quality indices.

Dr. Gabriel Cristobal
Dr. Saúl Blanco Lanza
Dr. Gloria Bueno
Prof. Jan Kybic
Guest Editors

Manuscript Submission Information

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Keywords

  • Image Acquisition
  • Segmentation, matching, simulation
  • Shape analysis
  • Live cycle analysis
  • Image analysis; Feature extraction
  • Discriminant analysis
  • Classification
  • Taxonomy
  • Software development for image processing and analysis
  • Forensic limnology;
  • Applications in the environment and earth sciences, e.g., oil and gas exploration, archaeology

Published Papers (4 papers)

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Research

Open AccessArticle A Tuning Method for Diatom Segmentation Techniques
Appl. Sci. 2017, 7(8), 762; https://doi.org/10.3390/app7080762
Received: 6 May 2017 / Revised: 14 June 2017 / Accepted: 22 June 2017 / Published: 27 July 2017
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Abstract
Phytoplankton such as diatoms or desmids are useful for monitoring water quality. Manual image analysis is impractical due to the huge diversity of this group of microalgae and its great morphological plasticity, hence the importance of automating the analysis procedure. High-resolution images of
[...] Read more.
Phytoplankton such as diatoms or desmids are useful for monitoring water quality. Manual image analysis is impractical due to the huge diversity of this group of microalgae and its great morphological plasticity, hence the importance of automating the analysis procedure. High-resolution images of phytoplankton cells can now be acquired by digital microscopes, which facilitate automating the analysis and identification process of specimens. Therefore, new systems of image analysis are potentially advantageous compared to manual methods of counting for solution identification. Segmentation is an important step in the analysis of phytoplankton images. Many standard techniques like thresholding and edge detection are employed in the segmentation of diatoms and other phytoplankton, which are crucial organisms in microscopy images. However, in general, they require several parameters to be fixed beforehand by the user in order to get the best results. This process is usually done by comparing results and looking for the best parameters. To automatize this process, we propose an automatic tuning method to find the optimal parameters in an iterative procedure, called Parametric Segmentation Tuning (PST). This technique compares successive segmentation results, choosing the ones that gets the maximal similarity. In this paper, tuning is formulated as an optimization problem using a similarity function within the solution space. This space consists of the set of binary images that are generated by the segmentation technique to be tuned, where these binary images are seen as a function of the original images and the segmentation parameters. The PST technique was tested with two of the most popular techniques employed to segment phytoplankton images: the Canny edge detection and a binarisation method. The results of the thresholding technique were validated by comparing them to those of the Otsu method and the Canny method with a ground truth. They show that PST is effective to find the best parameters. Full article
(This article belongs to the Special Issue Automated Analysis and Identification of Phytoplankton Images)
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Open AccessFeature PaperArticle Automated Diatom Classification (Part A): Handcrafted Feature Approaches
Appl. Sci. 2017, 7(8), 753; https://doi.org/10.3390/app7080753
Received: 31 May 2017 / Revised: 11 July 2017 / Accepted: 18 July 2017 / Published: 25 July 2017
Cited by 1 | PDF Full-text (18795 KB) | HTML Full-text | XML Full-text
Abstract
This paper deals with automatic taxa identification based on machine learning methods. The aim is therefore to automatically classify diatoms, in terms of pattern recognition terminology. Diatoms are a kind of algae microorganism with high biodiversity at the species level, which are useful
[...] Read more.
This paper deals with automatic taxa identification based on machine learning methods. The aim is therefore to automatically classify diatoms, in terms of pattern recognition terminology. Diatoms are a kind of algae microorganism with high biodiversity at the species level, which are useful for water quality assessment. The most relevant features for diatom description and classification have been selected using an extensive dataset of 80 taxa with a minimum of 100 samples/taxon augmented to 300 samples/taxon. In addition to published morphological, statistical and textural descriptors, a new textural descriptor, Local Binary Patterns (LBP), to characterize the diatom’s valves, and a log Gabor implementation not tested before for this purpose are introduced in this paper. Results show an overall accuracy of 98.11% using bagging decision trees and combinations of descriptors. Finally, some phycological features of diatoms that are still difficult to integrate in computer systems are discussed for future work. Full article
(This article belongs to the Special Issue Automated Analysis and Identification of Phytoplankton Images)
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Open AccessArticle Automated Diatom Classification (Part B): A Deep Learning Approach
Appl. Sci. 2017, 7(5), 460; https://doi.org/10.3390/app7050460
Received: 8 March 2017 / Revised: 21 April 2017 / Accepted: 21 April 2017 / Published: 2 May 2017
Cited by 3 | PDF Full-text (4602 KB) | HTML Full-text | XML Full-text
Abstract
Diatoms, a kind of algae microorganisms with several species, are quite useful for water quality determination, one of the hottest topics in applied biology nowadays. At the same time, deep learning and convolutional neural networks (CNN) are becoming an extensively used technique for
[...] Read more.
Diatoms, a kind of algae microorganisms with several species, are quite useful for water quality determination, one of the hottest topics in applied biology nowadays. At the same time, deep learning and convolutional neural networks (CNN) are becoming an extensively used technique for image classification in a variety of problems. This paper approaches diatom classification with this technique, in order to demonstrate whether it is suitable for solving the classification problem. An extensive dataset was specifically collected (80 types, 100 samples/type) for this study. The dataset covers different illumination conditions and it was computationally augmented to more than 160,000 samples. After that, CNNs were applied over datasets pre-processed with different image processing techniques. An overall accuracy of 99% is obtained for the 80-class problem and different kinds of images (brightfield, normalized). Results were compared to previous presented classification techniques with different number of samples. As far as the authors know, this is the first time that CNNs are applied to diatom classification. Full article
(This article belongs to the Special Issue Automated Analysis and Identification of Phytoplankton Images)
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Open AccessArticle Large-Scale Permanent Slide Imaging and Image Analysis for Diatom Morphometrics
Appl. Sci. 2017, 7(4), 330; https://doi.org/10.3390/app7040330
Received: 1 February 2017 / Revised: 16 March 2017 / Accepted: 23 March 2017 / Published: 28 March 2017
PDF Full-text (3490 KB) | HTML Full-text | XML Full-text | Supplementary Files
Abstract
Light microscopy analysis of diatom frustules is widely used in basic and applied research, notably taxonomy, morphometrics, water quality monitoring and paleo-environmental studies. Although there is a need for automation in these applications, various developments in image processing and analysis methodology supporting these
[...] Read more.
Light microscopy analysis of diatom frustules is widely used in basic and applied research, notably taxonomy, morphometrics, water quality monitoring and paleo-environmental studies. Although there is a need for automation in these applications, various developments in image processing and analysis methodology supporting these tasks have not become widespread in diatom-based analyses. We have addressed this issue by combining our automated diatom image analysis software SHERPA with a commercial slide-scanning microscope. The resulting workflow enables mass-analyses of a broad range of morphometric features from individual frustules mounted on permanent slides. Extensive automation and internal quality control of the results helps to minimize user intervention, but care was taken to allow the user to stay in control of the most critical steps (exact segmentation of valve outlines and selection of objects of interest) using interactive functions for reviewing and revising results. In this contribution, we describe our workflow and give an overview of factors critical for success, ranging from preparation and mounting through slide scanning and autofocus finding to final morphometric data extraction. To demonstrate the usability of our methods we finally provide an example application by analysing Fragilariopsis kerguelensis valves originating from a sediment core, which substantially extends the size range reported in the literature. Full article
(This article belongs to the Special Issue Automated Analysis and Identification of Phytoplankton Images)
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