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Principles of Bioimage Informatics: Focus on Machine Learning of Cell Patterns

  • Luis Pedro Coelho
  • Estelle Glory-Afshar
  • Joshua Kangas
  • Shannon Quinn
  • Aabid Shariff
  • Robert F. Murphy
Part of the Lecture Notes in Computer Science book series (LNCS, volume 6004)

Abstract

The field of bioimage informatics concerns the development and use of methods for computational analysis of biological images. Traditionally, analysis of such images has been done manually. Manual annotation is, however, slow, expensive, and often highly variable from one expert to another. Furthermore, with modern automated microscopes, hundreds to thousands of images can be collected per hour, making manual analysis infeasible.

This field borrows from the pattern recognition and computer vision literature (which contain many techniques for image processing and recognition), but has its own unique challenges and tradeoffs.

Fluorescence microscopy images represent perhaps the largest class of biological images for which automation is needed. For this modality, typical problems include cell segmentation, classification of phenotypical response, or decisions regarding differentiated responses (treatment vs. control setting). This overview focuses on the problem of subcellular location determination as a running example, but the techniques discussed are often applicable to other problems.

Keywords

Biomedical Image Fluorescence Microscope Image Biological Image Protein Subcellular Location Subcellular Location Pattern 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.

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Copyright information

© Springer-Verlag Berlin Heidelberg 2010

Authors and Affiliations

  • Luis Pedro Coelho
    • 1
    • 2
    • 3
  • Estelle Glory-Afshar
    • 3
    • 6
  • Joshua Kangas
    • 1
    • 2
    • 3
  • Shannon Quinn
    • 2
    • 3
    • 4
  • Aabid Shariff
    • 1
    • 2
    • 3
  • Robert F. Murphy
    • 1
    • 2
    • 3
    • 4
    • 5
    • 6
  1. 1.Joint Carnegie Mellon University–University of Pittsburgh Ph.D. Program in Computational Biology 
  2. 2.Lane Center for Computational BiologyCarnegie Mellon University 
  3. 3.Center for Bioimage InformaticsCarnegie Mellon University 
  4. 4.Department of Biological SciencesCarnegie Mellon University 
  5. 5.Machine Learning DepartmentCarnegie Mellon University 
  6. 6.Department of Biomedical EngineeringCarnegie Mellon University 

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