Robust segmentation of noisy images using a neural network model

https://doi.org/10.1016/0262-8856(92)90054-7Get rights and content

Abstract

In this paper we present a robust algorithm for segmentation of noisy images using a neural network. A neural network model is employed to represent a segmented image in such a way that the region type of each pixel simply corresponds to the neuron state. The image segmentation procedure consists of two phases: initial segmentation, and refining segmentation. The initial segmentation is implemented iteratively by using a dynamic evolution algorithm to minimize the energy function of the neural network. During the second phase, a learning procedure is described to determine the interconnection weights based on a multi-layer logic neural network, and the refining segmentation is achieved by a retrieval procedure. Several experimental results involving both synthetic and real images are reported.

References (16)

  • RJ Hathaway et al.

    Relational duals of the c-means clustering algorithms

    Patt. Recogn.

    (1989)
  • A Rosenfeld et al.

    Digital Picture Processing

    (1982)
  • DH Ballard et al.

    Computer Vision

    (1982)
  • J Besag

    Spatial interaction and statistical analysis of lattice systems

    J. Roy. Statist. Soc. B

    (1974)
  • H Derin et al.

    Modeling and segmentation of noisy and textured images using Gibbs random fields

    IEEE Trans. PAMI

    (1987)
  • S Lakshmanan et al.

    Simultaneous parameter estimation and segmentation of Gibbs random fields using simulated annealing

    IEEE Trans. PAMI

    (1989)
  • T Wang et al.

    A neural network model for image segmentation

    AMSE Review

    (1991)
  • C Cortes et al.

    A network system for image segmentation

There are more references available in the full text version of this article.

Cited by (0)

View full text
Copyright © 1992 Published by Elsevier B.V.