Machine Vision and Applications

, Volume 23, Issue 1, pp 15–24

The application of support vector machine classification to detect cell nuclei for automated microscopy

Authors

    • School of EngineeringCranfield University
  • Toby P. Breckon
    • School of EngineeringCranfield University
  • David A. Randell
    • Oral Pathology Unit, School of DentistryUniversity of Birmingham
  • Gabriel Landini
    • Oral Pathology Unit, School of DentistryUniversity of Birmingham
Original Paper

DOI: 10.1007/s00138-010-0275-y

Cite this article as:
Han, J.W., Breckon, T.P., Randell, D.A. et al. Machine Vision and Applications (2012) 23: 15. doi:10.1007/s00138-010-0275-y

Abstract

The detection of cell nuclei for diagnostic purposes is an important aspect of many medical laboratory examinations. Precise location of cell nuclei can aid in correct diagnosis and aid in automated microscopy applications, such as cell counting and tissue architecture analysis. In this paper, we investigate the use of support vector machine classification based on Laplace edge features for this task. Compared with existing applications, we used only one type of cell nucleus images to train the classifier but this classifier can locate other two types of cell nuclei with different stains and scales successfully. The results illustrate that such a data driven approach has remarkable detection and generalization performance.

Keywords

Cell nuclei detectionAutomated microscopySupport vector machines

Copyright information

© Springer-Verlag 2010