Paper Info
Title
Improving Medical Image Fusion Method Using Fuzzy Entropy And Nonsubsampling Contourlet Transform
Abstract
Many types of medical images must be fused, as single-modality medical images can only provide limited information due to the imaging principles and the complexity of human organ structures. In this paper, a multimodal medical image fusion method that combines the advantages of nonsubsampling contourlet transform (NSCT) and fuzzy entropy is proposed to provide a basis for clinical diagnosis and improve the accuracy of target recognition and the quality of fused images. An image is initially decomposed into low- and high-frequency subbands through NSCT. The corresponding fusion rules are adopted in accordance with the different characteristics of the low- and high-frequency components. The membership degree of low-frequency coefficients is calculated. The fuzzy entropy is also computed and subsequently used to guide the fusion of coefficients to preserve image details. High-frequency components are fused by maximizing the regional energy. The final fused image is obtained by inverse transformation. Experimental results show that the proposed method achieves good fusion effect based on the subjective visual effect and objective evaluation criteria. This method can also obtain high average gradient, SD, and edge preservation and effectively retain the details of the fused image. The results of the proposed algorithm can provide effective reference for doctors to assess patient condition.
Year
DOI
Venue
2021
10.1002/ima.22476
INTERNATIONAL JOURNAL OF IMAGING SYSTEMS AND TECHNOLOGY
Keywords
DocType
Volume
fuzzy entropy, image fusion, medical image, nonsubsampled contourlet transform, regional energy
Journal
31
Issue
ISSN
Citations 
1
0899-9457
0
PageRank 
References 
Authors
0.34
0
4
Name
Order
Citations
PageRank
Wei Li111121.02
Qinyong Lin221.02
Keqiang Wang300.34
Ken Cai433.43