关键词: 医学图像融合, 提升小波变换, 脉冲耦合神经网络, 非线性滤波万有引力, 链接强度

Abstract: In order to better meet the modern medicine clinical diagnosis and treatment needs, improve the medical image fusion quality, this paper proposes a new medical image fusion algorithm based on lifting wavelet transform, combined with the Pulse Coupled Neural Networks（PCNN） and pixel point of nonlinear filtering gravitation. A fusion rule based on the area average gray is adopted in low-frequency coefficients. Adaptive PCNN fusion rule is adopted in high-frequency coefficients. It makes the pixel’s nonlinear filtering gravitation as the linking strength of simplified PCNN model. So PCNN can adaptively control the size of linking strength, and according to the ignition matrix to determine the fusion of high-frequency coefficients. Experiments show that the fused image obtains more edge detail information than other methods and every index has improved. It has a better fusion performance.

Key words: medical image fusion, lifting wavelet transform, Pulse Coupled Neural Networks（PCNN）, nonlinear filtering gravitation, linking strength