Qian Mei This email address is being protected from spambots. You need JavaScript enabled to view it.1 and Mengfan Li1

1Zhengzhou Railway Vocational & Technical College, Zhengzhou 450000, China


Received: March 7, 2022
Accepted: April 24, 2022
Publication Date: May 13, 2022

 Copyright The Author(s). This is an open access article distributed under the terms of the Creative Commons Attribution License (CC BY 4.0), which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are cited.

Download Citation: ||https://doi.org/10.6180/jase.202302_26(2).0007  


In order to improve the quality of medical image fusion, preserve the spectral characteristics of the original image and avoid spectral degradation of the fused image, we propose a new medical image fusion method based on the nonsubsampled contourlet transform (NSCT) and adaptive pulse coupled neural network (PCNN). NSST is used to decompose the source image into high and low-frequency. The improved PCNN is used to fuse the low-frequency sub-band coefficients, and the square error sum of pixels is used as the excitation factor. The sum of directional gradients is selected as the link strength. The high-frequency sub-band coefficients with large computational load are fused by using the feature-based rule. The fused image is obtained by NSST inverse transformation. Experimental results show that compared with the NSCT and the other combinations of NSST and PCNN algorithms, the proposed algorithm can obtain better results, and the running time is shorter. The average values of information entropy, spatial frequency, standard deviation, clarity, edge information with proposed method are 6.6191, 23.3014, 64.3961, 9.4683, and 0.7213, respectively.

Keywords: NSCT, adaptive PCNN, feature-based rule, medical image fusion


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