Authors: P.D. Sathya; R. Kayalvizhi
Addresses: Faculty of Engineering and Technology, Department of Electrical Engineering, Annamalai University, Chidambaram 608002, Tamil Nadu, India ' Faculty of Engineering and Technology, Department of Instrumentation Engineering, Annamalai University, Chidambaram 608002, Tamil Nadu, India
Abstract: Segmentation is low-level image transformation routine that partitions an input image into distinct disjoint and homogeneous regions using thresholding algorithms. This paper presents both adaptation and comparison of four stochastic optimisation techniques to solve multilevel thresholding problem in image segmentation: Genetic Algorithm (GA), Particle Swarm Optimisation (PSO), Bacterial Foraging (BF) and Modified BF (MBF). Three objective functions such as Tsallis', Kapur's and Otsu's functions are considered and maximised by the above four algorithms. In order to compare the performances of all the algorithms, they are tested on various test images. Results show that the BF and MBF are much better in terms of robustness and time convergence than the PSO and GA. Among the last two algorithms, MBF is the most efficient with respect to the quality of the solution in terms of Peak Signal to Noise Ratio (PSNR) value and stability.
Keywords: image segmentation; multilevel thresholding; GAs; genetic algorithms; PSO; particle swarm optimisation; MBF; modified bacterial foraging; Tsallis; Kapur; Otsu; image transformation.
International Journal of Signal and Imaging Systems Engineering, 2012 Vol.5 No.1, pp.43 - 57
Available online: 04 May 2012 *Full-text access for editors Access for subscribers Purchase this article Comment on this article