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. © 2012 Springer-Verlag.
CITATION STYLE
Khan, A. M., Humayun, A., Raza, S. E. A., Khan, M., & Rajpoot, N. M. (2012). A novel paradigm for mining cell phenotypes in multi-tag bioimages using a locality preserving nonlinear embedding. In Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics) (Vol. 7666 LNCS, pp. 575–583). https://doi.org/10.1007/978-3-642-34478-7_70
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