A Nonsubsampled Contourlet Transform Based Medical Image Fusion Method

2013 ◽  
Vol 12 (4) ◽  
pp. 749-755 ◽  
Author(s):  
Shen Yu ◽  
Ren Enen ◽  
Dang Jian-Wu ◽  
Wang Guo-Hua ◽  
Feng Xin
Keyword(s):  
Image Fusion ◽  
Medical Image ◽  
Contourlet Transform ◽  
Fusion Method ◽  
Nonsubsampled Contourlet Transform ◽  
Medical Image Fusion ◽  
Image Fusion Method

scholarly journals Medical Image Fusion Algorithm Based on Nonlinear Approximation of Contourlet Transform and Regional Features

2017 ◽  
Vol 2017 ◽  
pp. 1-9 ◽  
Author(s):  
Hui Huang ◽  
Xi’an Feng ◽  
Jionghui Jiang
Keyword(s):  
Image Fusion ◽  
Medical Image ◽  
Sparse Matrix ◽  
Nonlinear Approximation ◽  
Low Frequency ◽  
Contourlet Transform ◽  
Fusion Algorithm ◽  
Medical Image Fusion ◽  
Regional Features ◽  
Pros And Cons

According to the pros and cons of contourlet transform and multimodality medical imaging, here we propose a novel image fusion algorithm that combines nonlinear approximation of contourlet transform with image regional features. The most important coefficient bands of the contourlet sparse matrix are retained by nonlinear approximation. Low-frequency and high-frequency regional features are also elaborated to fuse medical images. The results strongly suggested that the proposed algorithm could improve the visual effects of medical image fusion and image quality, image denoising, and enhancement.

scholarly journals The Nonsubsampled Contourlet Transform Based Statistical Medical Image Fusion Using Generalized Gaussian Density

2015 ◽  
Vol 2015 ◽  
pp. 1-13 ◽  
Author(s):  
Guocheng Yang ◽  
Meiling Li ◽  
Leiting Chen ◽  
Jie Yu
Keyword(s):  
Image Fusion ◽  
Medical Image ◽  
Low Frequency ◽  
Contourlet Transform ◽  
Nonsubsampled Contourlet Transform ◽  
Medical Image Fusion ◽  
Gaussian Density ◽  
Generalized Gaussian Density ◽  
Fusion Scheme ◽  
Jensen Shannon Divergence

We propose a novel medical image fusion scheme based on the statistical dependencies between coefficients in the nonsubsampled contourlet transform (NSCT) domain, in which the probability density function of the NSCT coefficients is concisely fitted using generalized Gaussian density (GGD), as well as the similarity measurement of two subbands is accurately computed by Jensen-Shannon divergence of two GGDs. To preserve more useful information from source images, the new fusion rules are developed to combine the subbands with the varied frequencies. That is, the low frequency subbands are fused by utilizing two activity measures based on the regional standard deviation and Shannon entropy and the high frequency subbands are merged together via weight maps which are determined by the saliency values of pixels. The experimental results demonstrate that the proposed method significantly outperforms the conventional NSCT based medical image fusion approaches in both visual perception and evaluation indices.

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