Machine Vision and Applications

, Volume 23, Issue 4, pp 659–673

Cell morphology classification and clutter mitigation in phase-contrast microscopy images using machine learning


  • Diane H. Theriault
    • Department of Computer ScienceBoston University
  • Matthew L. Walker
    • Department of BiologyBoston University
  • Joyce Y. Wong
    • Department of Biomedical EngineeringBoston University
    • Department of Computer ScienceBoston University
Special Issue Paper

DOI: 10.1007/s00138-011-0345-9

Cite this article as:
Theriault, D.H., Walker, M.L., Wong, J.Y. et al. Machine Vision and Applications (2012) 23: 659. doi:10.1007/s00138-011-0345-9


We propose using machine learning techniques to analyze the shape of living cells in phase-contrast microscopy images. Large scale studies of cell shape are needed to understand the response of cells to their environment. Manual analysis of thousands of microscopy images, however, is time-consuming and error-prone and necessitates automated tools. We show how a combination of shape-based and appearance-based features of fibroblast cells can be used to classify their morphological state, using the Adaboost algorithm. The classification accuracy of our method approaches the agreement between two expert observers. We also address the important issue of clutter mitigation by developing a machine learning approach to distinguish between clutter and cells in time-lapse microscopy image sequences.


Microscopy imagingCell morphologyAdaboostMachine learning

Copyright information

© Springer-Verlag 2011