Genetic algorithm implementation of stack filter design for image restoration

K. K. Delibasis, P. E. Undrill, G. G. Cameron

Research output: Contribution to journalArticlepeer-review

34 Citations (Scopus)


Stack filters are a class of nonlinear spatial operators used for noise suppression. Their design is formulated as an optimisation problem and genetic algorithms (GAs) are used to perform the configuration. Applying the mean absolute error (MAE) as the basis of an objective function, the stack filter is used to restore magnetic resonance (MR) images corrupted with uncorrelated additive noise from 10%, and 50%. The filter is trained on corresponding patches of the original and noisy image and then applied to the whole image. The outcomes are compared with the median filter and return a smaller MAE for all noise levels. The dependency of MAE on the training window size and the GA early termination is examined, showing that a reduction of 75% in computational complexity can be achieved by a 10% relaxation in the MAE. The design is then extended from 9-point to 13-point filters and by training on Poisson noise, the filter is applied to nuclear medicine bone scans where no absolute truth exists. Surface topology, image profiles and the measurement of relative contrast show its value in reducing noise whilst preserving contrast. Because of its computational complexity the process has been implemented as a distributed GA using the parallel virtual machine (PVM) software.
Original languageEnglish
Pages (from-to)177-183
Number of pages7
JournalIEE Proceedings - Vision, Image and Signal Processing
Issue number3
Publication statusPublished - 1996


  • biomedical NMR
  • medical image processing
  • image restoration
  • genetic algorithms
  • computational complexity
  • nonlinear filters
  • error analysis
  • bone
  • distributed algorithms
  • spatial filters
  • adaptive filters
  • distributed genetic algorithm
  • stack filter design
  • nonlinear spatial operators
  • noise suppression
  • optimisation problem
  • mean absolute error
  • objective function
  • magnetic resonance images
  • uncorrelated additive noise
  • noisy image
  • original image
  • median filter
  • noise levels
  • training window size
  • computational complexity reduction
  • Poisson noise
  • nuclear medicine bone scans
  • surface topology
  • image profiles
  • contrast
  • parallel virtual machine software
  • Biomedical magnetic resonance imaging
  • Biomedical image processing
  • Image restoration
  • Genetic algorithms
  • Complexity theory
  • Nonlinear filters
  • Error analysis
  • Bones
  • Distributed algorithms
  • Spatial filters
  • Adaptive filters


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