结合SIST和压缩感知的CT与MRI图像融合  

CT and MRI Medical Image Fusion Based on Shift-invariant Shearlet Transform and Compressed Sensing

作　　者：殷明[1] 段普宏 褚标[1] 梁翔宇[1] 

机构地区：[1]合肥工业大学数学学院,合肥230009

出　　处：《光电工程》2016年第8期47-52,共6页Opto-Electronic Engineering

基　　金：国家自然科学基金(11172086);安徽省自然科学基金(1308085MA09);安徽省教育厅自然科学研究重点项目(KJ2013A216)

摘　　要：为了增强医学图像融合质量,提出了一种基于平移不变剪切波(SIST)和压缩感知的CT和MRI图像融合方法。首先,将源CT与MRI图像经过SIST分解后得到低频子带和高频子带;其次,对低频子带,提出了一种结合新的改进空间频率、改进的区域加权能量和局部区域相似匹配度的融合规则;对于高频子带,提出了一种基于自适应2PCNN-CS的融合规则;最后通过SIST逆变换得到融合图像。实验表明:本文方法在客观指标和图像视觉效果上都优于传统的CT与MRI医学图像融合方法。In order to enhance the quality of medical image fusion, a novel CT and MRI image fusion algorithm is proposed based on Shift-invariant Shearlet Transform （SIST） and compressed sensing. Firstly, the source CT and MRI images are decomposed by SIST to obtain the low frequency sub-bands and high frequency sub-bands. Then, for the low frequency sub-band coefficients, a fusion rule method combining with a new improved spatial frequency, which improves regional weighted energy and local similarity matched degree, is presented. For high frequency sub-band coefficients, a scheme based on the theory of adaptive 2PCNN-CS is presented. Finally, the fused image is obtained by performing the inverse SIST. The experimental results show that the proposed approach can outperform the conventional CT and MRI images fusion methods in terms of both objective evaluation criteria and visual quality.

关 键 词：信号处理 平移不变剪切波 自适应双通道脉冲耦合神经网络 压缩感知 

分 类 号：TP391[自动化与计算机技术—计算机应用技术]