基于NSST域混合滤波与改进边缘检测PCNN的医学图像融合

Medical image fusion based on hybrid filtering and improved edge detection PCNN in NSST domain

针对传统医学图像融合中存在细节模糊、能量保存不完整、运行时间长等问题,提出一种基于非下采样剪切波(non-subsampled shearlet transform, NSST)域混合滤波与改进边缘检测脉冲耦合神经网络(pulse coupled neural network, PCNN)的医学图像融合方法。首先,利用YUV模型进行颜色空间转换分离出亮度通道Y,接着利用混合滤波分别对源核磁共振(magnetic resonance imaging, MRI)图像和亮度通道的灰度图像进行不同程度的增强。其次,采用NSST对增强后的MRI和亮度通道的灰度图像进行分解,得到高低频子带。低频子带使用修正的拉普拉斯能量和(weighted sum of eight-neighborhood-based modified Laplacian,WSEML)与局部区域能量加权和(weight local energy,WLE)的融合策略,高频子带采用改进边缘检测PCNN的融合策略。最后,经NSST逆变换得到融合图像。通过与其他6种融合方法对比,本文方法可以有效提高图像融合过程中的细节提取和能量保存,且整体算法运行效率高、可视性好。

Aiming at the problems of blurred details,incomplete energy preservation and long running time in traditional medical image fusion,a medical image fusion method based on hybrid filtering and improved edge detection pulse coupled neural network(PCNN)in non-subsampled shearlet transform(NSST)domain is proposed.Firstly,the YUV model is used to perform a color space conversion to separate the luminance channel Y,and then compound filter is used to enhance the source MRI image and the grayscale image of the luminance channel in different degrees.Secondly,the grayscale images of the enhanced magnetic resonance imaging(MRI)and luminance channels are decomposed using NSST to obtain the high and low frequency subbands.The low-frequency subband uses a fusion strategy with a modified Laplace energy sum and a local area energy weighted sum,and the high-frequency subband uses an improved edge detection PCNN fusion strategy.Finally,the fused images are obtained by NSST inverse transformation.By comparing with other six fusion methods,this method can effectively improve the detail extraction and energy preservation in the process of image fusion,and the overall algorithm operates with high efficiency and good visibility.