Multi-Modal Medical Image Fusion WithAdaptive WeightedCombinationofNSSTBandsUsing Chaotic Grey Wolf Optimization

C.S, ASHA and Lal, Shyam and PRABHU GURUPUR, VARADRAJ and Saxena, Prakash P U (2019) Multi-Modal Medical Image Fusion WithAdaptive WeightedCombinationofNSSTBandsUsing Chaotic Grey Wolf Optimization. IEEE Access, 7. pp. 40782-40796. ISSN 2169-3536

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Abstract

Recently,medicalimagefusionhasemergedasanimpressivetechniqueinmergingthemedical images of different modalities. Certainly, the fused image assists the physician in disease diagnosis for effective treatment planning. The fusion process combines multi-modal images to incur a single image with excellent quality, retaining the information of original images. This paper proposes a multi-modal medical image fusion through a weighted blending of high-frequency subbands of nonsubsampledshearlet transform (NSST) domain via chaotic grey wolf optimization algorithm. As an initial step, the NSST is applied on source images to decompose into the multi-scale and multi-directional components. The low-frequency bands are fused based on a simple max rule to sustain the energy of an individual. The texture detailsofinputimagesarepreservedbyanadaptivelyweightedcombinationofhigh-frequencyimagesusing a recent chaotic grey wolf optimization algorithm to minimize the distance between the fused image and source images. The entire process emphasizes on retaining the energy of the low-frequency band and the transferring of texture features from source images to the fused image. Finally, the fused image is formed using inverse NSST of merged low and high-frequency bands. The experiments are carried out on eight differentdiseasedatasetsobtainedfromBrainAtlas,whichconsistsofMR-T1andMR-T2,MRandSPECT, MR and PET, and MR and CT. The effectiveness of the proposed method is validated using more than 100 pairs of images based on the subjective and objective quality assessment. The experimental results confirm that the proposed method performs better in contrast with the current state-of-the-art image fusion techniques in terms of entropy, VIFF, and FMI. Hence, the proposed method will be helpful for disease diagnosis, medical treatmentplanning, and surgicalprocedure

Item Type: Article
Uncontrolled Keywords: NSST, grey wolf optimization, chaotic function, image fusion, MRI, PET, SPECT
Subjects: Medicine > KMC Mangalore > Radiotherapy and Oncology
Depositing User: KMCMLR User
Date Deposited: 28 Aug 2019 11:22
Last Modified: 28 Aug 2019 11:22
URI: http://eprints.manipal.edu/id/eprint/154465

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