Abstract
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.
Cite
CITATION STYLE
Masuda, A., Raytchev, B., Kurita, T., Imamura, T., Suzuki, M., Tamaki, T., & Kaneda, K. (2015). Automatic detection of good/bad colonies of iPS cells using local features. In Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics) (Vol. 9352, pp. 153–160). Springer Verlag. https://doi.org/10.1007/978-3-319-24888-2_19
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