基于形态学图像增强和PCNN的脑部CT与MRI图像融合  

Brain CT and MRI Image Fusion Based on Morphological Image Enhancement and PCNN

作　　者：李昌兴 雷柳 张晓璐 LI Chang-xing;LEI Liu;ZHANG Xiao-lu(School of Science,Xi’an University of Posts and Telecommunications,Xi’an 710121,China;School of Communication and Information Engineering,Xi’an University of Posts and Telecommunications,Xi’an 710121,China)

机构地区：[1]西安邮电大学理学院,西安710121 [2]西安邮电大学通信与信息工程学院,西安710121

出　　处：《计算机科学》2020年第10期194-199,共6页Computer Science

基　　金：陕西省自然科学基础研究计划资助项目(2018JM1054)。

摘　　要：图像在融合过程中容易引入伪吉布斯现象,在图像的边缘细节处容易产生“伪影”和振铃现象。针对以上问题,提出了一种基于形态学图像增强和脉冲耦合神经网络(Pulse Coupled Neural Network,PCNN)的脑部CT与MRI图像融合方法。首先基于形态学对源图像进行开运算和闭运算增强处理,再将增强处理过的图像作为PCNN接收域的输入激励,输入PCNN融合模型内,对模型输出的权重图进行判定,形成一幅清晰并且易于处理的图像。实验结果表明,所提方法在保持边缘清晰化、保留有效信息、平衡冗余现象方面都优于其他方法,经过形态学图像增强和PCNN融合后的图像相较于未经增强处理的PCNN方法所得图像的平均梯度提高了24.59%左右,空间频率提高了42.56%左右;相较于基于拉普拉斯的图像融合方法,图像的标准差提高了16.67%左右。To compensate for the problems that arise during brain CT and MRI image fusion,such as the false Gibbs pheno-menon,the lack of detailed information,ringing,a fusion method of brain CT and MRI image based on morphological image enhancement and the PCNN(Pulse Coupled Neural Network)is proposed.Firstly,the source image is enhanced by open and closed opera tion based on morphology.The enhancement processed image is input into the PCNN fusion model as an input stimulus of the PCNN receiving domain,to determine the final weight map of the model output.Finnaly,a clear and easily processed image is formed.Experimental results show that the proposed method is superior to other methods in maintaining image edge clearness,preserving effective information and balancing redundancy.Compared with the unenhanced PCNN method,the average gradient and spatial frequency of image after morphological enhancement and PCNN fusion increases by 24.59%and 42.56%respectively.Compared with the image fusion based on Laplacian method,the standard deviation increases by 16.67%.

关 键 词：形态学图像增强 脉冲耦合神经网络模型 融合权重 链接强度 医学图像融合 

分 类 号：TP391[自动化与计算机技术—计算机应用技术]