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Scholkopf, B., Burges, C.J.C. and Smola, A.J., Eds. (1998) Advances in Kernel Methods— Support Vector Learning. MIT Press.

has been cited by the following article:

• TITLE: Image Analysis in Microbiology: A Review

  AUTHORS: Evgeny Puchkov

  KEYWORDS: Computer Image Analysis, Microorganisms, Viability, Yeast; Bacteria, Fungi, Colony Counter, Microbial Identification, Multispectral Imaging, Hyperspectral Imaging, Diffraction Pattern Imaging, Scatter Pattern Imaging, Multifractal Analysis, Support Vector Machines, Principal Component Analysis, Linear Discriminant Analysi, ImageJ, Matlab, Fluorescence Microscopy, Microfluorimetry, Green Fluorescent Protein (GFP)

  JOURNAL NAME: Journal of Computer and Communications, Vol.4 No.15, November 28, 2016

  ABSTRACT: This review is focused on using computer image analysis as a means of objective and quantitative characterizing optical images of the macroscopic (e.g. microbial colonies) and the microscopic (e.g. single cell) objects in the microbiological research. This is the way of making many visual inspection assays more objective and less time and labor consuming. Also, it can provide new visually inaccessible information on relation between some optical parameters and various biological features of the microbial cul-tures. Of special interest is application of image analysis in fluorescence microscopy as it opens new ways of using fluorescence based methodology for single microbial cell studies. Examples of using image analysis in the studies of both the macroscopic and the microscopic microbiological objects obtained by various imaging techniques are presented and discussed.