Paper Info
Title
Partitioned-cooperative quantum-behaved particle swarm optimization based on multilevel thresholding applied to medical image segmentation.
Abstract
Display Omitted We introduce a novel quantum-behaved particle swarm optimization (SCQPSO) algorithm- SCQPSO.In SCQPSO, the auxiliary swarms and partitioned search space are introduced to increase the population diversity.In SCQPSO, the cooperative theory is introduced into QPSO algorithm to change the updating mode of the particles.SCQPSO is tested on five benchmark test functions and five shift complex functions.We use SCQPSO to optimize the parameters for OTSU image segmentation of four stomach CT images. In this paper, in order to search the global optimum solution with a very fast convergence speed across the whole search space, we propose a partitioned and cooperative quantum-behaved particle swarm optimization (SCQPSO) algorithm. The auxiliary swarms and partitioned search space are introduced to increase the population diversity. The cooperative theory is introduced into QPSO algorithm to change the updating mode of the particles in order to guarantee that this algorithm well balances the effectiveness and simplification. Firstly, we explain how this method leads to enhanced population diversity and improved algorithm over previous strategies, and emphasize this algorithm with comparative experiments using five benchmark test functions and five shift complex functions. After that we demonstrate a reasonable application of the proposed algorithm, by showing how it can be used to optimize the parameters for OTSU image segmentation for processing medical images. The results show that the proposed SCQPSO algorithm outperforms than the other improved QPSO in terms of the quality of the solution, and performs better for solving the image segmentation than the QPSO algorithm, the sunCQPSO algorithm, the CCQPSO algorithm.
Year
DOI
Venue
2017
10.1016/j.asoc.2017.03.018
Appl. Soft Comput.
Keywords
Field
DocType
Quantum-behaved particle swarm optimization,Cooperative theory,Multi-swarm,Artitioned search space,Global optimization
Particle swarm optimization,Convergence (routing),Population,Mathematical optimization,Global optimization,Image segmentation,Multi-swarm optimization,Artificial intelligence,Thresholding,Population-based incremental learning,Mathematics,Machine learning
Journal
Volume
Issue
ISSN
56
C
1568-4946
Citations 
PageRank 
References 
11
0.48
27
Authors
4
Name
Order
Citations
PageRank
Yangyang Li1439.03
Xiaoyu Bai2121.49
Licheng Jiao35698475.84
Yu Xue487160.17