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Improving medical image fusion method using fuzzy entropy and nonsubsampling contourlet transform
Author(s) -
Li Wei,
Lin Qinyong,
Wang Keqiang,
Cai Ken
Publication year - 2021
Publication title -
international journal of imaging systems and technology
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.359
H-Index - 47
eISSN - 1098-1098
pISSN - 0899-9457
DOI - 10.1002/ima.22476
Subject(s) - contourlet , image fusion , artificial intelligence , computer science , fuzzy logic , fusion rules , entropy (arrow of time) , pattern recognition (psychology) , fusion , inverse , computer vision , image (mathematics) , transformation (genetics) , mathematics , wavelet transform , wavelet , linguistics , philosophy , physics , geometry , quantum mechanics , biochemistry , chemistry , gene
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.