Abstract
This paper proposes a new multilevel thresholding method segmenting images based on particle swarm optimization (PSO). In the proposed method, the thresholding problem is treated as an optimization problem, and solved by using the principle of PSO. The algorithm of PSO is used to find the best values of thresholds that can give us an appropriate partition for a target image according to a fitness function. in this paper, a new quantitative evaluation function is proposed based on the information theory. The new evaluation function is used as an objective function for the algorithm of PSO in the proposed method. Because quantitative evaluation functions deal with segmented images as a set of regions, the target image is divided into a set of regions and not to a set of classes during the different stages of our method (where a region is a group of connected pixels having the same range of gray levels). The proposed method has been tested on different images, and the experimental results demonstrate its effectiveness.
Highlights
Swarm intelligence (SI) has been applied in numerous fields including optimization [1]
EXPERIMENTAL RESULTS In order to prove the efficiency and accuracy of PSOTH, it has been applied to many different images
It can be seen that the results shown in the figures clearly illustrate that the segmented images obtained by the PSOTH method are better than the segmented images obtained by the region based segmentation and the kmeans algorithm
Summary
Swarm intelligence (SI) has been applied in numerous fields including optimization [1]. One of SI methods performing well in solving optimization problems is particle swarm optimization (PSO). PSO is a stochastic search method that was developed in 1995 [1] based on the sociological behavior of bird flocking. The algorithm of PSO is easy to implement and has been successfully applied to solve a wide range of optimization problems in many fields such as image processing fields including image segmentation. Image segmentation is a low-level image processing task aiming at partitioning an image into homogeneous regions [2]. Image segmentation methods have been classified into numerous categories of which region and thersholding based segmentations
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