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.
Preview
Unable to display preview. Download preview PDF.
References
Cristianini, N., Shawe-Taylor, J.: An Introduction to Support Vector Machines and Other Kernel-Base Learning Methods. Cambridge University Press (2000)
Ebben, J.D., Zorniak, M., Clark, P.A., Kuo, J.S.: Introduction to induced pluripotent stem cells: Advancing the potential for personalized medicine. World Neurosurg. 76(3–4), 270–275 (2011)
Joutsijoki, H., et al.: Classification of ipsc colony images using hierarchical strategies with support vector machines. In: IEEE Symp. CIDM 2014, pp. 86–92 (2014)
Kazutoshi, T., Koji, T., Mari, O., Megumi, N., Tomoko, I., Kiichiro, T., Shinya, Y.: Induction of pluripotent stem cells from adult human fibroblasts by defined factors. Cell 131(5), 861–871 (2007)
Lowe, D.G.: Distinctive image features from scale-invariant keypoints. International Journal Computer Vision 60(2), 91–110 (2004)
Tax, D., Duin, R.: Data domain description by support vectors. In: Proceedings of ESANN 1999, pp. 251–256 (1999)
Watanabe, H., Tanabe, K., Kii, H., Ishikawa, M., Nakada, C., Uozumi, T., Kiyota, Y., Wada, Y., Tsuchiya, R.: Establishment of an algorithm for automated detection of ips/non-ips cells under a culture condition by noninvasive image analysis (2012)
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2015 Springer International Publishing Switzerland
About this paper
Cite this paper
Masuda, A. et al. (2015). Automatic Detection of Good/Bad Colonies of iPS Cells Using Local Features. In: Zhou, L., Wang, L., Wang, Q., Shi, Y. (eds) Machine Learning in Medical Imaging. MLMI 2015. Lecture Notes in Computer Science(), vol 9352. Springer, Cham. https://doi.org/10.1007/978-3-319-24888-2_19
Download citation
DOI: https://doi.org/10.1007/978-3-319-24888-2_19
Published:
Publisher Name: Springer, Cham
Print ISBN: 978-3-319-24887-5
Online ISBN: 978-3-319-24888-2
eBook Packages: Computer ScienceComputer Science (R0)