NSST和改进PCNN相结合的甲状腺图像融合

Thyroid Image Fusion Based on NSST and Improved PCNN

针对甲状腺B超图像的低对比度和SPECT图像的低空间分辨率的特点,提出了一种基于非下采样Shearlet变换(NSST)和改进脉冲耦合神经网络(PCNN)相结合的图像融合算法。本文用NSST将两幅经过精确配准的源图像分解,得到低频子带系数以及不同尺度和方向的高频子带系数。低频系数采取区域能量取大的融合规则,高频系数采取改进的PCNN算法,将改进的拉普拉斯能量和作为PCNN的输入项,梯度能量作为PCNN的链接强度,利用点火输出幅度总和取大的融合规则选择高频系数,最后通过NSST逆变换得到融合图像。实验结果表明,本文所提出的算法在主观视觉和客观标准上均取得良好的效果。

According to the characteristics of type-B ultrasonic image with low contrast and SPECT image with low spatial resolution, an image fusion algorithm based on Nonsubsampled Shearlet Transform（NSST） and improved Pulse Coupled Neural Network（PCNN） is proposed. The NSST is used to decompose two registered source images, and low frequency sub-band coefficients and high frequency sub-band coefficients with different scales and directions are obtained. Low frequency coefficients are fused by the maximum of the regional energy. High frequency coefficients are fused by improved PCNN algorithm. The Sum Modified Laplacian is used for the input of PCNN, and the Energy of Gradient is used for the link intensity of PCNN, thus the high-frequency coefficients are selected by the sum of ignition output amplitude maximum. Finally, the fused image is reconstructed by inverse NSST. Experimental results demonstrate that the proposed algorithm achieves good results in the subjective perspective and objective criteria.