Multimodal Medical Image Fusion Using Dual-Tree Complex Wavelet Transform (DTCWT) with Modified Lion Optimization Technique (mLOT) and Intensity Co-Variance Verification (ICV)
Keywords:
CT, DTCWT, ICV, low and high frequency, medical image fusion, mLOT, MRIAbstract
Image fusion is place as a key role in medical image investigation and preparation of treatments for bio-medical research and clinical diagnosis. The most incentive is fuse to capture a large amount of vital information from the input images to have its output image. In this paper, a well-organized multimodal medical image fusion approach is obtainable to fuse computer tomography (CT) and magnetic resonance image (MRI). The significant co-efficient of source images are line up through the dual-tree complex wavelet transform (DTCWT), followed by unit of low and high frequency components. Two completely different proposed fusion rules based on weighted fusion rule, the weights are optimized by modified lion optimization technique (mLOT) and intensity co-variance verification (ICV) are used to fuse the low and high frequency coefficient. The fused image is reconstructed by inverse DTCWT with all amalgamate co-efficient. To prove the potency of the new approach is greater than the wellknown standard algorithm, experiments are conducted. Based on experimental comparison and proposed approach, the better results are fused image quality are obtained. The studies of qualitative and quantitative metrics are clearly demonstrated that the new approach is to display the high superior than the present.
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