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Erschienen in:
2015 | OriginalPaper | Buchkapitel
Automatic Detection of Good/Bad Colonies of iPS Cells Using Local Features
verfasst von : Atsuki Masuda, Bisser Raytchev, Takio Kurita, Toru Imamura, Masashi Suzuki, Toru Tamaki, Kazufumi Kaneda
Erschienen in: Machine Learning in Medical Imaging
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In this paper we propose a method able to automatically detect good/bad colonies of iPS cells using local patches based on densely extracted SIFT features. Different options for local patch classification based on a kernelized novelty detector, a 2-class SVM and a local Bag-of-Features approach are considered. Experimental results on 33 images of iPS cell colonies have shown that excellent accuracy can be achieved by the proposed approach.