Pattern Recognition
Fuzzy entropy threshold approach to breast cancer detection
Information Sciences—Applications: An International Journal
A fast thresholding selection procedure for multimodal and unimodal histograms
Pattern Recognition Letters
Computational intelligence PC tools
Computational intelligence PC tools
A fast scheme for optimal thresholding using genetic algorithms
Signal Processing
Optimum Image Thresholding via Class Uncertainty and Region Homogeneity
IEEE Transactions on Pattern Analysis and Machine Intelligence
Image segmentation by histogram thresholding using fuzzy sets
IEEE Transactions on Image Processing
Supervised range-constrained thresholding
IEEE Transactions on Image Processing
MCPR'11 Proceedings of the Third Mexican conference on Pattern recognition
Image segmentation using Atanassov's intuitionistic fuzzy sets
Expert Systems with Applications: An International Journal
An artificial bee colony-least square algorithm for solving harmonic estimation problems
Applied Soft Computing
| Hi-index | 12.05 |
In this paper, we present a new variant of Particle Swarm Optimization (PSO) for image segmentation using optimal multi-level thresholding. Some objective functions which are very efficient for bi-level thresholding purpose are not suitable for multi-level thresholding due to the exponential growth of computational complexity. The present paper also proposes an iterative scheme that is practically more suitable for obtaining initial values of candidate multilevel thresholds. This self iterative scheme is proposed to find the suitable number of thresholds that should be used to segment an image. This iterative scheme is based on the well known Otsu's method, which shows a linear growth of computational complexity. The thresholds resulting from the iterative scheme are taken as initial thresholds and the particles are created randomly around these thresholds, for the proposed PSO variant. The proposed PSO algorithm makes a new contribution in adapting 'social' and 'momentum' components of the velocity equation for particle move updates. The proposed segmentation method is employed for four benchmark images and the performances obtained outperform results obtained with well known methods, like Gaussian-smoothing method (Lim, Y. K., & Lee, S. U. (1990). On the color image segmentation algorithm based on the thresholding and the fuzzy c-means techniques. Pattern Recognition, 23, 935-952; Tsai, D. M. (1995). A fast thresholding selection procedure for multimodal and unimodal histograms. Pattern Recognition Letters, 16, 653-666), Symmetry-duality method (Yin, P. Y., & Chen, L. H. (1993). New method for multilevel thresholding using the symmetry and duality of the histogram. Journal of Electronics and Imaging, 2, 337-344), GA-based algorithm (Yin, P. -Y. (1999). A fast scheme for optimal thresholding using genetic algorithms. Signal Processing, 72, 85-95) and the basic PSO variant employing linearly decreasing inertia weight factor.