  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.
