Chapter

Focus on Bio-Image Informatics

Volume 219 of the series Advances in Anatomy, Embryology and Cell Biology pp 263-272

Date:

Bioimage Informatics for Big Data

  • Hanchuan PengAffiliated withAllen Institute for Brain Science Email author 
  • , Jie ZhouAffiliated withDepartment of Computer Science, Northern Illinois University
  • , Zhi ZhouAffiliated withAllen Institute for Brain Science
  • , Alessandro BriaAffiliated withDepartment of Engineering, University Campus Bio-Medico of RomeDepartment of Electrical and Information Engineering, University of Cassino and L.M.
  • , Yujie LiAffiliated withAllen Institute for Brain ScienceDepartment of Computer Science, University of Georgia
  • , Dean Mark KleissasAffiliated withJohns Hopkins University Applied Physics Laboratory
  • , Nathan G. DrenkowAffiliated withJohns Hopkins University Applied Physics Laboratory
  • , Brian LongAffiliated withAllen Institute for Brain Science
  • , Xiaoxiao LiuAffiliated withAllen Institute for Brain Science
    • , Hanbo ChenAffiliated withAllen Institute for Brain ScienceDepartment of Computer Science, University of Georgia

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Abstract

Bioimage informatics is a field wherein high-throughput image informatics methods are used to solve challenging scientific problems related to biology and medicine. When the image datasets become larger and more complicated, many conventional image analysis approaches are no longer applicable. Here, we discuss two critical challenges of large-scale bioimage informatics applications, namely, data accessibility and adaptive data analysis. We highlight case studies to show that these challenges can be tackled based on distributed image computing as well as machine learning of image examples in a multidimensional environment.