Stack Filter Design Using a Distributed Parallel Implementation of Genetic Algorithms

Peter E. Undrill (University of Aberdeen, UK)

Kostas Delibasis (University of Aberdeen, UK)

George G. Cameron (University of Aberdeen, UK)

Abstract: Stack filters are a class of non-linear spatial operators used for suppression of noise in signals. In this work their design is formulated as an optimisation problem and a method that uses Genetic Algorithms (GAs) to perform the configuration is explained. Because of its computational complexity the process has been implemented as a distributed parallel GA using the Parallel Virtual Machine (PVM) software. We present the results of applying our stack filters to the restoration of magnetic resonance (MR) images corrupted with uniform, uncorellated, noise showing improved statistical performance compared with the median filter and indicating better retention of image details. The efficiency of the parallel implementation is examined, addressing both algorithmic and data decomposition, showing that execution times can be significantly reduced by distributing the task across a network of heterogeneous processors.

Keywords: Distributed Computation, Genetic Algorithms, Image Processing, Stack Filters

Categories: D.3.2, G.1.0, G.1.6, G.3, I.4