Modern Trends in Diatom Identification (original) (raw)
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A low-cost automated digital microscopy platform for automatic identification of diatoms
Applied Sciences, 2020
Currently, microalgae (i.e., diatoms) constitute a generally accepted bioindicator of water quality and therefore provide an index of the status of biological ecosystems. Diatom detection for specimen counting and sample classification are two difficult time-consuming tasks for the few existing expert diatomists. To mitigate this challenge, in this work, we propose a fully operative low-cost automated microscope, integrating algorithms for: (1) stage and focus control, (2) image acquisition (slide scanning, stitching, contrast enhancement), and (3) diatom detection and a prospective specimen classification (among 80 taxa). Deep learning algorithms have been applied to overcome the difficult selection of image descriptors imposed by classical machine learning strategies. With respect to the mentioned strategies, the best results were obtained by deep neural networks with a maximum precision of 86{\%} (with the YOLO network) for detection and 99.51{\%} for classification, among 80 different species (with the AlexNet network). All the developed operational modules are integrated and controlled by the user from the developed graphical user interface running in the main controller. With the developed operative platform, it is noteworthy that this work provides a quite useful toolbox for phycologists in their daily challenging tasks to identify and classify diatoms.
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.
Automatic Identification of Diatoms using Multi-scale Mathematical Morphology
2009
Diatoms are unicellular algae with a great ecological importance. They are ornamented with patterns that are characteristic for the species they belong to. Till now these diatoms are identified by experts, which is a time-consuming and tedious process. For this reason and due to a lack of diatomists, the Automatic Diatom Identification And Classification project ADIAC was started, where various techniques from image analysis are investigated for this use. The goal of this research was to investigate the possibilities of the identification of diatoms using mathematical morphology. Images are considered by this approach as mathematical entities such as sets or functions on which operators are defined. These operators are used to compute pattern spectra that describe the presence or absence of image details with certain characteristics such as size or shape. The idea of this approach is that the patterns on the diatoms can be described by these pattern spectra, so that an identificatio...