摘要
提出一种利用基于梯度加权的灰度归一化互信息相似性测度,并采用凹函数递减的权衡比重的粒子群优化算法作为搜索策略的多模图像精确配准方法。传统的基于灰度互信息配准方法往往只考虑灰度相关性,忽略或不当引入图像空间特征信息,配准容易陷入局部极值,从而出现误配。将灰度与梯度特征有效融合,即梯度加权到灰度互信息中,同时考虑了2幅图像的灰度统计相关性和图像空间特征信息,提高了多模图像配准的精度与稳定性。通过对遥感图像的拟配准与MR-PET医学图像的实际配准,证明了该方法效果良好,算法稳定,配准的准确率和参数精度都得到明显的提高。
This paper proposed an accurate method of multi-mode image registration based on gray-scale mutual information and gradient weighted normalized mutual information as a similarity measuring,and decreasing concave function of the trade-off proportion of the improved particle swarm optimization（PSO） as a search strategy.Registration method based on gray-scale mutual information often considers only grayscale,ignoring or introducing improperly the feature information in image space.As a result,the registration was easy to fall into local minima,and mismatches.The proposed algorithm merged the gradient weighted-to-gray and the mutual information,taking into account the gray statistical correlation of two images and the image spatial characteristics,so as to improve the multi-mode image registration accuracy and stability.Simulation and practical registration on remote sensing images and MRI-PET medical image registration showed the algorithm＇s good performance：the algorithm was stable and had a high registration precision and parameter accuracy.
出处
《南昌大学学报（工科版）》
CAS
2012年第4期396-400,共5页
Journal of Nanchang University(Engineering & Technology)
基金
江西省自然科学基金资助项目(2010GQS0166)
江西省教育厅科技资助项目(GJJ10191)
关键词
互信息
图像配准
梯度特征
优化算法
mutual information
image registration
gradient feature
optimization algorithm
