基于非下采样Contourlet变换的自适应医学图像融合算法

doi:10.3969/j.issn.0255-8297.2017.06.010

Abstract

Abstract:

An adaptive image fusion algorithm based on non-subsampled contourlet transform(NSCT)is proposed for medical images. Source images are first registered and then decomposed to low and high frequency sub-bands using NSCT. The NSCT coefficients in each sub-band are fused. For coefficients in the low frequency bands, a fusion rule based on regional energy, mutual information and information entropy is used. In high frequency bands, sum of modified Laplacian is used. The final image is obtained from the fused sub-images in the low and high frequency bands using inverse NSCT. Experiments are conducted for gray and color images to compare the propose method with previous algorithms. The results show that fused images using the proposed method contains more texture information, and is visually better.

Key words: medical image fusion, sum of modified Laplacian energy, non-subsampled contourlet transform (NSCT), regional energy

CLC Number:

TP391.4

LOU Jian-qiang, DAI Wen-zhan, LI Jun-feng. Adaptive Fusion Algorithm for Medical Images Using Non-subsampled Contourlet Transform[J]. Journal of Applied Sciences, 2017, 35(6): 763-774.

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Adaptive Fusion Algorithm for Medical Images Using Non-subsampled Contourlet Transform

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