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.
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.
Metal pollution of aquatic habitats is a major and persistent environmental problem. Acid mine drainage (AMD) affects lotic systems in numerous and interactive ways. In the present work, a mining area (Roșia Montană) was chosen as study site, and we focused on two aims: (i) to find the set of environmental predictors leading to the appearance of the abnormal diatom individuals in the study area and (ii) to assess the relationship between the degree of valve outline deformation and AMD-derived pollution. In this context, morphological differences between populations of Achnanthidium minutissimum and A. macrocephalum, including normal and abnormal individuals, were evidenced by means of valve shape analysis. Geometric morphometry managed to capture and discriminate normal and abnormal individuals. Multivariate analyses (NMDS, PLS) separated the four populations of the two species mentioned and revealed the main physico-chemical parameters that influenced valve deformation in this context, namely conductivity, Zn, and Cu. ANOSIM test evidenced the presence of statistically significant differences between normal and abnormal individuals within both chosen Achnanthidium taxa. In order to determine the relative contribution of each of the measured physico-chemical parameters in the observed valve outline deformations, a PLS was conducted, confirming the results of the NMDS. The presence of deformed individuals in the study area can be attributed to the fact that the diatom communities were strongly affected by AMD released from old mining works and waste rock deposits.
Accurate taxonomic resolution in light microscopy analyses of microalgae is essential to achieve high quality, comparable results in both floristic analyses and biomonitoring studies. A number of closely related diatom taxa have been detected to date co-occurring within benthic diatom assemblages, sharing many morphological, morphometrical and ecological characteristics. In this contribution, we analysed the hypothesis that, where a large sample size (number of individuals) is available, common morphometrical parameters (valve length, width and stria density) are sufficient to achieve a correct identification to the species level. We focused on some common diatom taxa belonging to the genus Gomphonema. More than 400 valves and frustules were photographed in valve view and measured using Fiji software. Several statistical tools (mixture and discriminant analysis, k-means clustering, classification trees, etc.) were explored to test whether mere morphometry, independently of other valve features, leads to correct identifications, when compared to identifications made by experts. In view of the results obtained, morphometry-based determination in diatom taxonomy is discouraged.
Many biological objects are barely distinguished with the brightfield microscope because they appear transparent, translucent and colourless. One simple way to make such specimens visible without compromising contrast and resolution is by controlling the amount and the directionality of the illumination light. Oblique illumination is an old technique described by many scientists and microscopists that however has been largely neglected in favour of other alternative methods. Oblique lighting is created by illuminating the sample by only a portion of the light coming from the condenser. If properly used it can improve the resolution and contrast of transparent specimens such as diatoms. In this paper a quantitative evaluation of OL in brigthfield microscopy is presented. Several feature descriptors were selected for characterising contrast and sharpness showing that in general OL provides better performance for distinguishing minute details compared to other lighting modalities. Oblique lighting is capable to produce directionally shadowed differential contrast images allowing to observe phase details in a similar way to differential contrast images (DIC) but at lower cost. The main advantage of OL is that the resolution of the light microscope can be increased by effectively doubling the angular aperture. OL appears as a cost-effective technique both for the amateur and professional scientist that can be used as a replacement of DIC or phase contrast when resources are scarce.
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