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Counting colonies of bacteria can be a tedious and difficult task if it is done by hand. MATLAB and machine learning tools may
allow the process to be automated. One challenge to automating the process is ensuring that clusters are not counted. In this
MATLAB?s Image Toolbox
were used to train a system to count non-clustered colonies. Images simulating
real images were used in order to train the system. MATLAB was used to get region properties, such as eccentricity, axis length
and perimeter, for each object in a series of training images. Those properties were then tested in Weka to determine a suitable
classifier to identify non-clustered colonies. The
Na?ve Bayesian Classifier
was selected and applied to predict the classification
results in new images.
Maria Muradas-Lopez has completed his Ph.D. at the age of 25 years from Andhra University and postdoctoral studies from Stanford University
School of Medicine. He is the director of a premier Bio-Soft service organization. He has published more than 25 papers in reputed journals and has
been serving as an editorial board member of repute.
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