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Automatic Detection of Good/Bad Colonies of iPS Cells Using Local Features

  • Atsuki Masuda
  • Bisser RaytchevEmail author
  • Takio Kurita
  • Toru Imamura
  • Masashi Suzuki
  • Toru Tamaki
  • Kazufumi Kaneda
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 9352)

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.

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Copyright information

© Springer International Publishing Switzerland 2015

Open Access This chapter is licensed under the terms of the Creative Commons Attribution-NonCommercial 2.5 International License (http://creativecommons.org/licenses/by-nc/2.5/), which permits any noncommercial use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license and indicate if changes were made.

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Authors and Affiliations

  • Atsuki Masuda
    • 1
  • Bisser Raytchev
    • 1
    Email author
  • Takio Kurita
    • 1
  • Toru Imamura
    • 2
    • 3
  • Masashi Suzuki
    • 3
  • Toru Tamaki
    • 1
  • Kazufumi Kaneda
    • 1
  1. 1.Department of Information EngineeringHiroshima UniversityHiroshimaJapan
  2. 2.School of Bioscience and BiotechnologyTokyo University of TechnologyTokyoJapan
  3. 3.Biotechnology Research Institute for Drug DiscoveryAISTTokyoJapan

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