Classification of environmental microorganisms in microscopic images using shape features and support vector machines

Environmental Microorganisms (EMs) are currently recognised using molecular biology (DNA, RNA) or morphological methods. The first ones are very time-consuming and expensive. The second ones require a very experienced laboratory operator. To overcome these problems, we introduce an automatic classification method for EMs in the framework of content-based image analysis in this paper. To describe the shapes of EMs observed in microscopic images, we use Edge Histograms, Fourier Descriptors, extended Geometrical Features, as well as introduce Internal Structure Histograms. For classification, multi-class Support Vector Machine is applied to EMs represented by the above features. In order to quantitatively evaluate discriminative properties of the feature spaces we have introduced, we perform comprehensive experiments with a ground truth of manually segmented microscopic EM images. The best classification result of 89.7% proves a high robustness of our method in this application domain.