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

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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.

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Authors and Affiliations

  1. School of Engineering, Cranfield University, Cranfield, MK43 0AL, UK

    Ji Wan Han & Toby P. Breckon

  2. Oral Pathology Unit, School of Dentistry, University of Birmingham, St. Chad’s Queensway, Birmingham, B4 6NN, UK

    David A. Randell & Gabriel Landini

Authors
  1. Ji Wan Han
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  2. Toby P. Breckon
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  3. David A. Randell
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  4. Gabriel Landini
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Correspondence to Ji Wan Han.

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Han, J.W., Breckon, T.P., Randell, D.A. et al. The application of support vector machine classification to detect cell nuclei for automated microscopy. Machine Vision and Applications 23, 15–24 (2012). https://doi.org/10.1007/s00138-010-0275-y

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  • DOI: https://doi.org/10.1007/s00138-010-0275-y

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