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Artificial Neural Networks in Medicine and Biology pp 75–80Cite as

Cancerous Liver Tissue Differentiation Using LVQ

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

Abstract

Medical image processing technique provides an objective and quantitative approach for characterizing the pathological tissue images. In this paper, the fractal dimension was used to quantify the image textures for differentiating the normal and cancerous cells in the liver tissue image. Several image enhancement methods and one edge detection method were applied for accentuating the objects of interest before fractal dimension estimation. From the results, it is shown that the edge-based histogram equalization would be the best one among all these image enhancement methods. In addition, any two fractal dimensions were combined as the input features to the learning vector quantization network for tissue classification. From the results obtained from ten normal and ten cancer cases, the accuracy was demonstrated to be more than 90%. Above all, the user-friendly graphical user interface was also developed in this study.

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References

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Author information

Authors and Affiliations

  1. Institute of Biomedical Engineering, National Cheng Kung University, Tainan, Taiwan, Roc.

    Kuo-Sheng Cheng & Richard Sun

  2. Department of Pathology, National Cheng Kung University Hospital Tainan, Taiwan, ROC.

    Nan-Haw Chow

Authors
  1. Kuo-Sheng Cheng
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  2. Richard Sun
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  3. Nan-Haw Chow
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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

Cheng, KS., Sun, R., Chow, NH. (2000). Cancerous Liver Tissue Differentiation Using LVQ. 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_9

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  • DOI: https://doi.org/10.1007/978-1-4471-0513-8_9

  • 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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