基于小波变换模极大值的医学图像融合技术

Fusion Technology of Medical Image Based on Wavelet Transform Modulus Maximum

目前的融合算法不能有效解决强去噪能力和细节信息保留之间的矛盾,为此,文中提出一种基于小波变换模极大值特征的窗口区域强度自适应加权平均融合算法.首先,用选定的小波基提取不同尺度下的小波变换模极大值特性;然后,利用信号与噪声的Lipschitz指数在局部奇异处呈现不同表现形式的特性,滤除噪声信号;接着,计算模极大值的局部区域强度,动态地实现不同尺度子图像的小波分解系数之间的权重分配;最后,将处理后的子图像重建得到融合后的图像.对计算机断层图像和正电子发射断层图像的融合实验证明,所提出的算法既可以适应不同特征图像的融合任务,又能在保留细节信息的基础上有效抑制噪声.

As the existing fusion algorithms cannot effectively overcome the contradiction between the strong denoising ability and the detailed information reservation, a new fusion algorithm based on the characteristics of wavelet transform modulus maximum is proposed. In the proposed algorithm obtained from the adaptive weighted averaging of the window-region intensity, first, the characteristics of wavelet transform modulus maximum in different scales are extracted with a chosen wavelet radix. Next, the noise is filtered according to the different characteristics of Lipschitz index between the signal and the noise at local singularity points. Then, the local region intensity of the modulus maximum is calculated, and the weights of wavelet coefficients are dynamically distributed in the sub-images in different scales. Finally, the sub-images are reconstructed to obtain a fused image. The fusion experiments of computed tomography and positron emission tomography images indicate that the proposed method can adapt itself to various fusion demands and can restrain the noise with the detailed information being reserved.