基于人眼视觉特性的NSCT医学图像自适应融合

医学图像融合对于临床诊断具有重要的应用价值。针对多模态医学图像特性，本文提出一种基于人类视觉特性的医学图像自适应融合方法。首先，对经配准的源图像进行非间隔采样轮廓变换（（Nonsubsampled Coutour-let，NSCT）多尺度分解，得到低频子带和若干高频方向子带；其次，根据低频子带集中了大部分源图像能量和决定图像轮廓的特点，采用区域能量与平均梯度相结合的方法进行融合；根据人眼对图像对比度及边缘、纹理的高敏感度，在高频子带系数的选取时提出区域拉普拉斯能量、方向对比度与脉冲耦合神经网络（Pulse Coupled Neural Network，PCNN）相结合的融合策略；进而，提出了把与人类视觉高度一致的加权结构相似度（Weighted Structure Similarity，WSSIM）作为图像融合目标函数，自适应地获取各子带的最优权值；最后，对灰度图像和彩色图像进行了大量融合比较实验，并对不同融合方法进行分析对比。实验结果表明：本文算法不仅可以有效保留源图像的信息，而且可以使融合图像灰度级更分散，更好地保留了图像边缘信息，具有更好的视觉效果。

Adaptive MedicaI Image Fusion Based on Human VisuaI Features

Medical image fusion has very important application value for medical image analysis and diseases diagno-sis.According to the characteristics of multi modality medical image and human visual features,a new medical image fusion algorithm in NSCT (nonsubsampled coutourlet,NSCT)domain is proposed.Firstly,source images after registration are de-composed into low and high frequency sub-bands using NSCT.According to the low frequency subbands concentrating the majority energy of the source image and determining the image coutour,a fusion rule based on weighted region average ener-gy combined with average gradient is adopted in low frequency subband coefficients.Moreover,according to human visual system which is more sensitive to contrast and edge,texture of image,the fusion strategy based on directive contrast integrat-ed with the improved energy of Laplacian and PCNN (Pulse Coupled Neural Network,PCNN)are used to fuse high-fre-quency subbands.Furthermore,a closed loop feedback is introduced into the fusion rules of low and high frequency subbands to obtain optimal fused weights adaptively by using WSSIM (Weighted Structure Similarity,WSSIM)which highly consist-ent with the HVS(human visual features,HVS)as objective function.Finally,a lot of experiments of fusion of images inclu-ding gray images and color images based on different fusion methods are conducted.The experiment results are analyzed in terms of visual quality and objective evaluation.The experiment results show that the proposed algorithm can effectively pre-serve information and significantly improve the performance of fusion image in terms of quantity of information,dispersed gray scale,visual quality and objective evaluation index.