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Fusion of mis‐registered GFP and phase contrast images with convolutional sparse representation and adaptive region energy rule
Author(s) -
Qiu Chenhui,
Peng Wenxian,
Wang Yuanyuan,
Hong Jiang,
Xia Shunren
Publication year - 2020
Publication title -
microscopy research and technique
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.536
H-Index - 118
eISSN - 1097-0029
pISSN - 1059-910X
DOI - 10.1002/jemt.23385
Subject(s) - fusion , robustness (evolution) , artificial intelligence , computer science , image fusion , pattern recognition (psychology) , fusion rules , contrast (vision) , representation (politics) , fusion protein , computer vision , image (mathematics) , chemistry , gene , philosophy , biochemistry , linguistics , politics , political science , law , recombinant dna
Biomedical image fusion is the process of combining the information from different imaging modalities to get a synthetic image. Fusion of phase contrast and green fluorescent protein (GFP) images is significant to predict the role of unknown proteins, analyze the function of proteins, locate the subcellular structure, and so forth. Generally, the fusion performance largely depends on the registration of GFP and phase contrast images. However, accurate registration of multi‐modal images is a very challenging task. Hence, we propose a novel fusion method based on convolutional sparse representation (CSR) to fuse the mis‐registered GFP and phase contrast images. At first, the GFP and phase contrast images are decomposed by CSR to get the coefficients of base layers and detail layers. Secondly, the coefficients of detail layers are fused by the sum modified Laplacian (SML) rule while the coefficients of base layers are fused by the proposed adaptive region energy (ARE) rule. ARE rule is calculated by discussion mechanism based brain storm optimization (DMBSO) algorithm. Finally, the fused image is achieved by carrying out the inverse CSR. The proposed fusion method is tested on 100 pairs of mis‐registered GFP and phase contrast images. The experimental results reveal that our proposed fusion method exhibits better fusion results and superior robustness than several existing fusion methods.