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A novel multimodal medical image fusion using sparse representation and modified spatial frequency
Author(s) -
Aishwarya N.,
Bennila Thangammal C.
Publication year - 2018
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.22268
Subject(s) - computer science , discriminative model , sparse approximation , artificial intelligence , image fusion , pattern recognition (psychology) , dictionary learning , image (mathematics) , fusion , domain (mathematical analysis) , representation (politics) , key (lock) , computation , k svd , fusion rules , algorithm , mathematics , mathematical analysis , linguistics , philosophy , computer security , politics , political science , law
Abstract Fusion of multimodal imaging data supports medical experts with ample information for better disease diagnosis and further clinical investigations. Recently, sparse representation (SR)‐based fusion algorithms has been gaining importance for their high performance. Building a compact, discriminative dictionary with reduced computational effort is a major challenge to these algorithms. Addressing this key issue, we propose an adaptive dictionary learning approach for fusion of multimodal medical images. The proposed approach consists of three steps. First, zero informative patches of source images are discarded by variance computation. Second, the structural information of remaining image patches is evaluated using modified spatial frequency (MSF). Finally, a selection rule is employed to separate the useful informative patches of source images for dictionary learning. At the fusion step, batch‐OMP algorithm is utilized to estimate the sparse coefficients. A novel fusion rule which measures the activity level in both spatial domain and transform domain is adopted to reconstruct the fused image with the sparse vectors and trained dictionary. Experimental results of various medical image pairs and clinical data sets reveal that the proposed fusion algorithm gives better visual quality and competes with existing methodologies both visually and quantitatively.