基于NSCT域滚动引导滤波与自适应PCNN的医学图像融合.

Aiming at the problems of blurring edge contours and loss of texture details after fusion of conventional CT and MRI medical images, this paper proposed an image fusion method based on non-subsampled contourlet transform domain combined with phase consistent rolling guidance filtering (PCRGF) and improved parameter-adaptive dual channel pulse coupled neural network (PCNN) .Firstly, it used PCRGF to enhance the CT source images to improve the definition of bone contour structure. Then, it applied NSCT to decompose enhanced CT and MRI source images to obtain the high and low frequency sub-bands.Fusion of low frequency sub-band used an improved parameter-adaptive dual channel pulse coupled neural network, which significantly improved the blurring of texture details in soft tissue.It used a weighted summation modified Laplace (WSML) algorithm to fuse the high frequency sub-bands, which enhanced the fused image with more details, textures and other information in the source images. Finally, it used the inverse NSCT transformation to reconstruct the fused image.The results of five groups of comparison experiments show that the objective evaluation indexes of AG,CC,SF,MSE and CEN are improved by 13.30%,6.71%,4.40%,40.23% and 19.16%,indicate that this method performs better in enhancing the texture details, edge contours, structural similarity and image pixels of the source image. [ABSTRACT FROM AUTHOR]