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Classification of Yeast Cells from Image Features to Evaluate Pathogen Conditions
| Content Provider | Semantic Scholar |
|---|---|
| Author | Puttena, Peter Van Der Bertensa, Laura Liua, Jinshuo Hagenb, Ferry Boekhoutb, Teun |
| Copyright Year | 2006 |
| 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 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. |
| File Format | PDF HTM / HTML |
| Alternate Webpage(s) | http://www.liacs.nl/~putten/library/200701YeastpaperSPIE-v21.pdf |
| Alternate Webpage(s) | http://liacs.leidenuniv.nl/~puttenpwhvander/library/200701YeastpaperSPIE-v21.pdf |
| Language | English |
| Access Restriction | Open |
| Subject Keyword | Academy Algorithm Benchmark (computing) Binary image Class Classification Collections (publication) Computer vision Cryptococcus neoformans Data mining Feature extraction Feature selection Genetic Heterogeneity Holism Image analysis Infection Meningitis Morphometrics Pathogenic organism Physical object Pneumonia Preprocessor Science Sensor Simulation Thickness (graph theory) VOS or OpenVOS algorithm |
| Content Type | Text |
| Resource Type | Article |
