Morphometrics from images, image analysis, may reveal differences between classes of objects present in the images.
We have performed an image-features-based classification for the pathogenic yeast Cryptococcus neoformans. Building
and analyzing image collections from the yeast under different environmental or genetic conditions may help to
diagnose a new "unseen" situation. Diagnosis here means that retrieval of the relevant information from the image
collection is at hand each time a new "sample" is presented. The basidiomycetous yeast Cryptococcus neoformans can
cause infections such as meningitis or pneumonia. The presence of an extra-cellular capsule is known to be related to
virulence. This paper reports on the approach towards developing classifiers for detecting potentially more or less
virulent cells in a sample, i.e. an image, by using a range of features derived from the shape or density distribution. The
classifier can henceforth be used for automating screening and annotating existing image collections. In addition we will
present our methods for creating samples, collecting images, image preprocessing, identifying "yeast cells" and creating
feature extraction from the images. We compare various expertise based and fully automated methods of feature
selection and benchmark a range of classification algorithms and illustrate successful application to this particular
domain.
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