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A Learning by Sample Approach for the Detection of Features in Medical Images.

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Artificial Neural Networks in Medicine and Biology

Part of the book series: Perspectives in Neural Computing ((PERSPECT.NEURAL))

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Abstract

The detection of specific features in medical images is often a key support for diagnosis. Taking advantage of large bases of images where features of interest have been localized by clinicians, a modular system has been developed to spot similar features on new images. Images are scanned through a window, the size of which being previously fitted to the feature of interest. The recognition process involves a coding phase followed by a classification phase. These phases rely on unsupervised and supervised learning respectively for their implementation. Applications in Dermatology and Ophthalmology are presented.

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Author information

Authors and Affiliations

  1. INSERM — U 494 — CHU Pitié-Salpêtrière, 91 boulevard de l’hôpital, 75634, Paris cedex 13, France

    Camille Serruys, Djamel Brahmi, Alain Giron, Nathalie Cassoux, Raoul Triller, Phuc Le Huang & Bernard Fertil

Authors
  1. Camille Serruys
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  2. Djamel Brahmi
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  3. Alain Giron
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  4. Nathalie Cassoux
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  5. Raoul Triller
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  6. Phuc Le Huang
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  7. Bernard Fertil
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Editor information

Editors and Affiliations

  1. Department of Philosophy, Göteborg University, Box 200, SE-405 30, Göteborg, Sweden

    Helge Malmgren BA, PhD, MD

  2. Department of Electrical Engineering, Linköping University, SE-585 83, Linköping, Sweden

    Magnus Borga MSc, PhD

  3. Department of Computer Science, University of Skövde, PO Box 408, SE-541 28, Skövde, Sweden

    Lars Niklasson BSc, MSc, PhD

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© 2000 Springer-Verlag London

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Cite this paper

Serruys, C. et al. (2000). A Learning by Sample Approach for the Detection of Features in Medical Images.. In: Malmgren, H., Borga, M., Niklasson, L. (eds) Artificial Neural Networks in Medicine and Biology. Perspectives in Neural Computing. Springer, London. https://doi.org/10.1007/978-1-4471-0513-8_14

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  • DOI: https://doi.org/10.1007/978-1-4471-0513-8_14

  • Publisher Name: Springer, London

  • Print ISBN: 978-1-85233-289-1

  • Online ISBN: 978-1-4471-0513-8

  • eBook Packages: Springer Book Archive

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