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An improved multimodal medical image fusion scheme based on hybrid combination of nonsubsampled contourlet transform and stationary wavelet transform
Author(s) -
Ramlal Sharma Dileepkumar,
Sachdeva Jainy,
Ahuja Chirag Kamal,
Khandelwal Niranjan
Publication year - 2019
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.22310
Subject(s) - contourlet , artificial intelligence , image fusion , computer science , pattern recognition (psychology) , fusion rules , wavelet transform , fusion , inverse , wavelet , computer vision , mathematics , image (mathematics) , linguistics , philosophy , geometry
This research proposes an improved hybrid fusion scheme for non‐subsampled contourlet transform (NSCT) and stationary wavelet transform (SWT). Initially, the source images are decomposed into different sub‐bands using NSCT. The locally weighted sum of square of the coefficients based fusion rule with consistency verification is used to fuse the detailed coefficients of NSCT. The SWT is employed to decompose approximation coefficients of NSCT into different sub‐bands. The entropy of square of the coefficients and weighted sum‐modified Laplacian is employed as the fusion rules with SWT. The final output is obtained using inverse NSCT. The proposed research is compared with existing fusion schemes visually and quantitatively. From the visual analysis, it is observed that the proposed scheme retained important complementary information of source images in a better way. From the quantitative comparison, it is seen that this scheme gave improved edge information, clarity, contrast, texture, and brightness in the fused image.