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- W1966372021 abstract "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 Cryptococcus neoformans . Buildingand analyzing image collections from the under different environmental or genetic conditions may help todiagnose a new unseen situation. Diagnosis here means that retrieval of the relevant information from the imagecollection is at hand each time a new sample is presented. The basidiomycetous Cryptococcus neoformans cancause infections such as meningitis or pneumonia. The presence of an extra-cellular capsule is known to be related tovirulence. This paper reports on the approach towards developing classifiers for detecting potentially more or lessvirulent in a sample, i.e. an image, by using a range of features derived from the shape or density distribution. Theclassifier can henceforth be used for automating screening and annotating existing image collections. In addition we willpresent our methods for creating samples, collecting images, image preprocessing, identifying yeast cells and creatingfeature extraction from the images. We compare various expertise based and fully automated methods of featureselection and benchmark a range of classification algorithms and illustrate successful application to this particulardomain." @default.
- W1966372021 created "2016-06-24" @default.
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- W1966372021 date "2007-01-28" @default.
- W1966372021 modified "2023-09-23" @default.
- W1966372021 title "Classification of yeast cells from image features to evaluate pathogen conditions" @default.
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- W1966372021 doi "https://doi.org/10.1117/12.714072" @default.
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