Quantum-behaved particle swarm optimization for medical image registration
被引:0
作者:
Xie, Jingquan
论文数: 0引用数: 0
h-index: 0
机构:
Southern Yangtze Univ, Sch Informat Technol, Wuxi 214122, Peoples R ChinaSouthern Yangtze Univ, Sch Informat Technol, Wuxi 214122, Peoples R China
Xie, Jingquan
[1
]
Wang, Daojun
论文数: 0引用数: 0
h-index: 0
机构:
Southern Yangtze Univ, Sch Informat Technol, Wuxi 214122, Peoples R ChinaSouthern Yangtze Univ, Sch Informat Technol, Wuxi 214122, Peoples R China
Wang, Daojun
[1
]
Xu, Wenbo
论文数: 0引用数: 0
h-index: 0
机构:
Southern Yangtze Univ, Sch Informat Technol, Wuxi 214122, Peoples R ChinaSouthern Yangtze Univ, Sch Informat Technol, Wuxi 214122, Peoples R China
Xu, Wenbo
[1
]
机构:
[1] Southern Yangtze Univ, Sch Informat Technol, Wuxi 214122, Peoples R China
来源:
DCABES 2007 PROCEEDINGS, VOLS I AND II
|
2007年
关键词:
medical image registration;
feature control points;
quantum-behaved particle swarm optimization (QPSO);
D O I:
暂无
中图分类号:
TP3 [计算技术、计算机技术];
学科分类号:
0812 ;
摘要:
Medical image registration is the first step in the image fusion and other imaging processes. In this paper, the image edges are first detected by using canny operators, then the contour feature points are extracted by K-Means algorithm, and translation parameters are calculated by using Quantum-behaved Particle Swarm Optimization (QPSO) algorithm. Experiments indicate that the QPSO algorithm get better fitness comparing with the PSO algorithm and GA, it's suitable for medical image registration.