Abstract：Objective In this paper, a novel method for the fusion of multimodal medical images is proposed based on nonsubsampled
contourlet transform (NSCT). Methods Firstly, the source medical images were initially transformed by NSCT followed
by fusing low and high frequency sub-bands. Secondly, the low frequency components of NSCT were fused by the maximum local
mean scheme and high frequency components were fused by the maximum local variance scheme. Thirdly, the use of variance
enhanced the fusion scheme by preserving the edges in the images. These combinations significantly preserved more details in the
source images and improved the quality of the fused images. Finally, the fused image was reconstructed by inverse non-subsampled
contourlet transform. Results The efficiency of the suggested technique was carried out by fusion experiments on 6 different
multimodality medical image pairs, visually and quantitatively experimental results indicated that the percentage improvement
in entropy, mean, standard deviation and edge strength in proposed method were 0~40%, 3%~42%, 1%~42%, and 0.4%~48%
compared to conventional methods for 6 pairs of medical images. Conclusion The proposed method can obtain more efficient and
accurate fusion results. It can provide better robustness, superiority and become a feasible image fusion algorithm.
徐磊，曹艳. 基于NSCT的多模态医学图像融合算法的研究[J]. 中国医疗设备, 2017, 32(12): 63-67.
XU Lei, CAO Yan. A Novel Method for Multimodal Medical Image Fusion Based on
Non-Subsampled Contourlet Transform. China Medical Devices, 2017, 32(12): 63-67.
