Automatic Localization of Interest Points in Zebrafish Images with Tree-Based Methods

  • Olivier Stern
  • Raphaël Marée
  • Jessica Aceto
  • Nathalie Jeanray
  • Marc Muller
  • Louis Wehenkel
  • Pierre Geurts
Part of the Lecture Notes in Computer Science book series (LNCS, volume 7036)

Abstract

In many biological studies, scientists assess effects of experimental conditions by visual inspection of microscopy images. They are able to observe whether a protein is expressed or not, if cells are going through normal cell cycles, how organisms evolve in different experimental conditions, etc. But, with the large number of images acquired in high-throughput experiments, this manual inspection becomes lengthy, tedious and error-prone. In this paper, we propose to automatically detect specific interest points in microscopy images using machine learning methods with the aim of performing automatic morphometric measurements in the context of Zebrafish studies. We systematically evaluate variants of ensembles of classification and regression trees on four datasets corresponding to different imaging modalities and experimental conditions. Our results show that all variants are effective, with a slight advantage for multiple output methods, which are more robust to parameter choices.

Keywords

Regression Tree Local Binary Pattern Interest Point Central Pixel Single Output 
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 2011

Authors and Affiliations

  • Olivier Stern
    • 1
  • Raphaël Marée
    • 1
    • 2
  • Jessica Aceto
    • 3
  • Nathalie Jeanray
    • 3
  • Marc Muller
    • 3
  • Louis Wehenkel
    • 1
  • Pierre Geurts
    • 1
  1. 1.GIGA-Systems Biology and Chemical Biology, Dept. of EE and CSUniversity of LiègeBelgium
  2. 2.GIGA Bioinformatics Core FacilityUniversity of LiègeBelgium
  3. 3.GIGA-Development, Stem Cells and Regenerative Medicine, Molecular Biology and Genetic EngineeringUniversity of LiègeBelgium

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