A novel paradigm for mining cell phenotypes in multi-tag bioimages using a locality preserving nonlinear embedding

  • Authors:

  • Adnan Mujahid Khan;Ahmad Humayun;Shan-e-Ahmad Raza;Michael Khan;Nasir M. Rajpoot

  • Affiliations:

  • Department of Computer Science, University of Warwick, UK;Georgia Institute of Technology, Atlanta;Department of Computer Science, University of Warwick, UK;Department of Life Sciences, University of Warwick, UK;Department of Computer Science, University of Warwick, UK

  • Venue:
  • ICONIP'12 Proceedings of the 19th international conference on Neural Information Processing - Volume Part IV
  • Year:
  • 2012

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Abstract

Multi-tag bioimaging systems such as the toponome imaging system (TIS) require sophisticated analytical methods to extract molecular signatures of various types of cells. In this paper, we present a novel paradigm for mining cell phenotypes based on their high-dimensional co-expression profiles contained within the images generated by the robotically controlled TIS microscope installed at Warwick. The proposed paradigm employs a refined cell segmentation algorithm followed by a locality preserving nonlinear embedding algorithm which is shown to produce significantly better cell classification and phenotype distribution results as compared to its linear counterpart.