Automatic Classification of Nanoplankton Using a Neural Network on Color Fluorescence Microscope ImagesX&a1r0C

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While significant progress has been made recently in color image- analyzed microscopy, considerable intervention by a skilled microscopist is still required for visual classification of cell types. An accurate, rapid classification method could significantly improve the automation of color image-analyzed fluorescence microscopy for estimating plankton cell size and biomass and could benefit environmental managers and policy makers concerned with the quality of coastal and estuarine waters. This project will investigate an automatic classification technique which mimics human visual classification. A multilayered perception neural network on color image features will be trained to perform this classification in real time. The image data will be obtained from whole water plankton samples from estuarine, coastal and oceanic environments which have been prepared using standard techniques in image analyzed fluorescence microscopy.