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CT image segmentation by self-organizing learning

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New Trends in Neural Computation (IWANN 1993)

Part of the book series: Lecture Notes in Computer Science ((LNCS,volume 686))

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Abstract

In this paper we approach the segmentation of tibia CT images using a self-organizing feature map. This type of Artificial Neural Network carries out a competitive learning process which permits the discrimination of different structures found in the images with sensitivity to changes in the distribution and value of the gray levels of the pixels. The results obtained show that this technique is adequate for the segmentation of images with complex structures and a low signal/noise ratio.

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References

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Author information

Authors and Affiliations

  1. Departamento de Electrónica y Computación. Facultad de Física, Universidad de Santiago de Compostela, Spain

    D. Cabello, M. G. Penedo, S. Barro & J. M. Pardo

  2. Servicio de Cirugía Ortopédica. Hospital General de Galicia, Universidad de Santiago de Compostela, Spain

    J. Heras

Authors
  1. D. Cabello
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  2. M. G. Penedo
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  3. S. Barro
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  4. J. M. Pardo
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  5. J. Heras
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Editor information

José Mira Joan Cabestany Alberto Prieto

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© 1993 Springer-Verlag Berlin Heidelberg

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Cabello, D., Penedo, M.G., Barro, S., Pardo, J.M., Heras, J. (1993). CT image segmentation by self-organizing learning. In: Mira, J., Cabestany, J., Prieto, A. (eds) New Trends in Neural Computation. IWANN 1993. Lecture Notes in Computer Science, vol 686. Springer, Berlin, Heidelberg. https://doi.org/10.1007/3-540-56798-4_216

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  • DOI: https://doi.org/10.1007/3-540-56798-4_216

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  • Publisher Name: Springer, Berlin, Heidelberg

  • Print ISBN: 978-3-540-56798-1

  • Online ISBN: 978-3-540-47741-9

  • eBook Packages: Springer Book Archive

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