基于PCNN和指数型模糊加权熵的医学图像融合  

Medical image fusion based on PCNN and exponentially weighted fuzzy entropy

作　　者：姚丽莎[1] 赵海峰[1] 罗斌[1] 

机构地区：[1]安徽大学计算智能与信号处理教育部重点实验室,合肥230039

出　　处：《计算机工程与应用》2011年第25期198-201,共4页Computer Engineering and Applications

基　　金：国家自然科学基金(No.60772122);安徽省教育厅自然科学研究项目(No.KJ2008A145)~~

摘　　要：为了更好地对CT和MRI图像进行融合,提出了一种指数型模糊加权熵自适应融合规则和改进的PCNN区域信息融合规则在多小波基的框架下进行CT和MRI医学图像的融合方法。对待融合图像进行多小波基的分解,对不同频率分量采用不同融合算法。实验表明,算法明显优于其他融合算法。它提高了图像的清晰度,较大程度保留了细节信息,具有边缘信息突出,亮度对比度高的优点。In order to integrate CT and MRI images to be better,this paper presents a new CT and MRI medical image fusion method.This method proposes adaptive exponentially weighted fuzzy entropy fusion rules and improved regional information PCNN fusion rules.It is in the framework of multi-wavelet for CT and MRI medical image fusion.The images are decomposed based on multi-wavelets.Different image fusion algorithms are used for different frequency components.Experiments show that the proposed algorithm is superior to other fusion algorithms.It improves the clarity of the image and retains the details of the image in large extent,with the advantages of prominent edge information and high brightness and contrast.

关 键 词：指数型模糊熵 多小波基 脉冲耦合神经网络 图像融合 

分 类 号：TP391.41[自动化与计算机技术—计算机应用技术]