The objective of this research was to develop an automated system using image processing and statistical modeling techniques to identify and enumerate bacteria on slides containing Salmonella typhimurium. Pictures of bacterial cells were acquired with a CCD camera attached to a motorized fluorescence microscope. A shape boundary modeling technique, based on the use of circular autoregressive model parameters, was used. A minimum-distance classifier was trained with ten images belonging to each shape class (rod shape and circle shape). Experimental results showed that the model parameters could be used as descriptors of shape boundaries detected in digitized binary images of bacterial cells. In spite of the advantages of human vision, the differences between the computer and a bacteriologist in recognizing and counting of Salmonella cells were less than 8%. ne computer analyzed each image in approximately 5 s (a total of 2 h including sample preparation), while the bacteriologist spent an average of 1 min for each image.
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