Application of content-based image analysis to environmental microorganism classification

• Autorzy: Li C. , Shirahama K. , Grzegorzek M.

Identyfikatory

DOI

10.1016/j.bbe.2014.07.003

• Języki publikacji: EN

Abstrakty

EN

Environmental microorganisms (EMs) are single-celled or multi-cellular microscopic organ-isms living in the environments. They are crucial to nutrient recycling in ecosystems as they act as decomposers. Occurrence of certain EMs and their species are very informative indicators to evaluate environmental quality. However, the manual recognition of EMs in microbiological laboratories is very time-consuming and expensive. Therefore, in this article an automatic EM classification system based on content-based image analysis (CBIA) techniques is proposed. Our approach starts with image segmentation that determines the region of interest (EM shape). Then, the EM is described by four different shape descriptors, whereas the Internal Structure Histogram (ISH), a new and original shape feature extraction technique introduced in this paper, has turned out to possess the most discriminative properties in this application domain. Afterwards, for each descriptor a support vector machine (SVM) is constructed to distinguish different classes of EMs. At last, results of SVMs trained for all four feature spaces are fused in order to obtain the final classification result. Experimental results certify the effectiveness and practicability of our automatic EM classification system.

Słowa kluczowe

EN

environmental microorganism; classification; microscopic images; image segmentation; shape features; support vector machine (SVM); late fusion

PL

mikroorganizm środowiskowy; obraz mikroskopowy; segmentacja obrazu; maszyna wektorów wspierających

• Wydawca: Nałęcz Institute of Biocybernetics and Biomedical Engineering of the Polish Academy of Sciences ; Elsevier
• Czasopismo: Biocybernetics and Biomedical Engineering
• Rocznik: 2015
• Tom: Vol. 35, no. 1
• Strony: 10--21
• Opis fizyczny: Bibliogr. 40 poz., rys., tab., wykr.

Twórcy

autor

Li C.

• Research Group for Pattern Recognition, Department ETI, University of Siegen, Hoelderlinstr. 3, D-57076 Siegen, Germany

autor

Shirahama K.

• Research Group for Pattern Recognition, Department ETI, University of Siegen, Hoelderlinstr. 3, D-57076 Siegen, Germany

autor

Grzegorzek M.

• Research Group for Pattern Recognition, Department ETI, University of Siegen, Hoelderlinstr. 3, D-57076 Siegen, Germany

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