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Robust Neuron Counting Based on Fusion of Shape Map and Multi-cue Learning

  • Alexander Ekstrom
  • Randall W. Suvanto
  • Tao Yang
  • Bing Ye
  • Jie ZhouEmail author
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 9919)

Abstract

Automatic counting of neurons in fluorescently stained microscopic images is increasingly important for brain research when big imagery data sets are becoming a norm and will be more so in the future. In this paper, we present an automatic learning-based method for effective detection and counting of neurons with stained nuclei. A shape map that reflects the boosted edge and shape information is generated and a learning problem is formulated to detect the centers of stained nuclei. The method combines multiple cues of edge gradient, shape, and texture during shape map generation, feature extraction and final count determination. The proposed algorithm consistently delivers robust count ratios and precision rates on neurons in mouse and rat brain images that are shown to be better than alternative unsupervised and supervised counting methods.

Keywords

Neuron counting Machine learning Shape map Microscopic neuronal image Nuclei staining 

Notes

Acknowledgements

We thank Dr. Dragan Maric for providing the image for Bioimage Informatics Conference 2015 Nucleus Counting Challenge. The work was partially supported by NIH NIMH R15 MH099569 (Zhou) and R21 NS094091 from NIH and a Seed Grant from the Brain Research Foundation (Ye).

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Copyright information

© Springer International Publishing AG 2016

Authors and Affiliations

  • Alexander Ekstrom
    • 1
  • Randall W. Suvanto
    • 1
  • Tao Yang
    • 2
  • Bing Ye
    • 2
  • Jie Zhou
    • 1
    Email author
  1. 1.Department of Computer ScienceNorthern Illinois UniversityDeKalbUSA
  2. 2.Life Sciences Institute and Department of Cell and Developmental BiologyUniversity of MichiganAnn ArborUSA

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