Automatic Identification of Scenedesmus Polymorphic Microalgae from Microscopic Images

TL;DR

This paper presents an automated image analysis method for identifying and classifying Scenedesmus microalgae coenobia, achieving high accuracy and offering a reliable alternative to manual counting in bioprocess applications.

Contribution

It introduces a novel automated approach combining image preprocessing, segmentation, and feature extraction for microalgae identification and classification.

Findings

Classification accuracy of over 98% using SVM and ANN

Method reduces error compared to manual counting

Publicly available database for benchmarking

Abstract

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. 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. 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. Classification of coenobia achieves accuracies of 98.63% and 97.32% with Support Vector Machine (SVM) and Artificial Neural Network (ANN), respectively. According to the results it is possible to consider the proposed methodology as an alternative to the traditional technique for algae counting. The database used in this paper is publicly available for download.