SBIR-STTR Award

While significant progress has been made recently in color imageanalyzed epifluorescence microscopy for the purpose of counting and measuring marine pico and nanoplankton cells, considerable intervention by a skilled microscopist is still required for classification of cell types. These classifications are currently performed visually using color information derived either from added fluorescent stains (fluorochromes) or autofluorescence of photopigments. 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 policymakers concerned with the quality of coastal and estuarine waters. The objective of this research is to develop an automatic classification technique which mimics human visual classification. The specific task is the automatic classification of nanoplankton-sized particles as detritus, heterotrophic, chlorophyll-dominant phototrophs, or phycoerythrin-dominant phototrophs. Investigators will train a multilayered perceptron neural network on color image features, which are derived from the hue-saturation-value color space, to perform this classification task 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.The potential commercial application as described by the awardee: The techniques developed here will form a significant component of a planned automated image processing workstation which is dedicated to fluorescence microscopy. Application areas for such a system include marine biology and biomedical research.