A new Optimization-Based Image Segmentation method By Particle Swarm Optimization

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.

@article{Mohsen2011,
  title     = {A new Optimization-Based Image Segmentation method By Particle Swarm Optimization},
  journal   = {International Journal of Advanced Computer Science and Applications},
  volume    = {1},
  number    = {1},
  year      = {2011},
  publisher = {The Science and Information Organization},
  note      = {Special Issue on Image Processing and Analysis},
  author    = {Fahd M.A Mohsen and Mohiy M. Hadhoud and Khalid Amin},
  doi       = {10.14569/SpecialIssue.2011.010102},
  url       = {https://doi.org/10.14569/SpecialIssue.2011.010102}
}