Multi-modal MRI image fusion of the brain based on joint bilateral filter and non-subsampled shearlet transform Online publication date: Tue, 04-Apr-2023
by Changhan Meng; Mengxing Huang; Yuchun Li; Yu Zhang; Siling Feng; Yuanyuan Wu
International Journal of Bio-Inspired Computation (IJBIC), Vol. 21, No. 1, 2023
Abstract: Multi-modal brain MRI image fusion is one of the hottest discussed issues in the current research of medical image processing and has a deep impact on brain science and diagnosis. In this study, a fusion algorithm based on the joint bilateral filter (JBF) and the non-subsampled shearlet transform (NSST) is proposed. First, the multi-modal brain MRI images were decomposed by NSST and JBF models to derive the high-frequency component and energy layer. Secondly, the corresponding energy layer images and high-frequency components are fused. Thirdly, the inverse NSST transform is performed on the energy layer fusion image and the high-frequency fusion image to obtain the ultimate fusion image. Finally, the algorithm was evaluated using a publicly available brain dataset. The experimental results show that the algorithm achieves good performance in terms of both subjective evaluation and objective metrics.
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