Original Paper

Machine Vision and Applications

, Volume 23, Issue 1, pp 15-24

First online:

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

  • Ji Wan HanAffiliated withSchool of Engineering, Cranfield University Email author 
  • , Toby P. BreckonAffiliated withSchool of Engineering, Cranfield University
  • , David A. RandellAffiliated withOral Pathology Unit, School of Dentistry, University of Birmingham
  • , Gabriel LandiniAffiliated withOral Pathology Unit, School of Dentistry, University of Birmingham

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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 detection Automated microscopy Support vector machines