Automatic identification of Scenedesmus polymorphic microalgae from microscopic images

Microalgae counting is used to measure biomass quantity. Usually, it is performed in a manual way using a Neubauer chamber and expert criterion, with the risk of a high error rate. Scenedesmus algae can build coenobia consisting of 1, 2, 4 and 8 cells. The amount of algae of each coenobium helps to...

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Bibliographic Details
Published in:Pattern analysis and applications : PAA 2018-05, Vol.21 (2), p.601-612
Main Authors: Giraldo-Zuluaga, Jhony-Heriberto, Salazar, Augusto, Diez, German, Gomez, Alexander, Martínez, Tatiana, Vargas, J. F., Peñuela, Mariana
Format: Article
Language:English
Subjects:
Algae
Artificial neural networks
Computer Science
Digital imaging
Equalization
Food processing industry
Image contrast
Image segmentation
Industrial and Commercial Application
Lipids
Methodology
Neural networks
Pattern Recognition
Proteins
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Summary:Microalgae counting is used to measure biomass quantity. Usually, it is performed in a manual way using a Neubauer chamber and expert criterion, with the risk of a high error rate. Scenedesmus algae can build coenobia consisting of 1, 2, 4 and 8 cells. The amount of algae of each coenobium helps to determine the amount of lipids, proteins, and other substances in a given sample of a algae crop. The knowledge of the quantity of those elements improves the quality of bioprocess applications. This paper addresses the methodology for automatic identification of Scenedesmus microalgae (used in the methane production and food industry) and applies it to images captured by a digital microscope. The use of contrast adaptive histogram equalization for pre-processing, and active contours for segmentation are presented. The calculation of statistical features (histogram of oriented gradients, Hu and Zernike moments) with texture features (Haralick and local binary patterns descriptors) are proposed for algae characterization. Classification of coenobia achieves accuracies of 98.63% and 97.32% with support vector machine and artificial neural network, respectively. According to the results, it is possible to consider the proposed methodology as an alternative to the traditional technique for algae counting. In addition, the database used for the developing of the proposed methodology is publicly available.
ISSN:1433-7541
1433-755X
DOI:10.1007/s10044-017-0662-3