M3IF‐(SWT‐TVC): Multi‐Modal Medical Image Fusion via Weighted Energy, Contrast in the SWT Domain, and Total Variation Minimization With Chambolle's Algorithm

ABSTRACT The multi‐modal medical image fusion (M3IF) combines the required and important information from different medical imaging modalities (computed tomography [CT], magnetic resonance imaging (MRI), positron emission tomography [PET], and single photon emission computed tomography [SPECT]) to provide single informative image. M3IF provides enhanced patient diagnosis, and precise treatment planning. This paper proposes a hybrid M3IF where input medical images are decomposed using stationary wavelet transform (SWT) into low‐frequency components (LFCs) and high‐frequency components (HFCs). The LFCs and HFCs are fused using energy‐ and contrast‐based metrics. And later reconstruction is performed using inverse SWT (ISWT). The total variation minimization (TVM) using Chambolle's algorithm is applied as a post‐refinement operation to reduce noise and preserves the fine details. In this paper, the proposed methodology is termed as M3IF‐(SWT‐TVC), Here, the acronym TVC is the combination of TVM using Chambolle's algorithm. TVM refinement process is an iterative approach, with the fusion outcomes of M3IF‐(SWT‐TVC) assessed over a predefined 100 iterations. The TVM, and SWT are blended to balance smoothness and structural details. The final fusion results obtained through M3IF‐(SWT‐TVC) are evaluated against several prominent non‐traditional methods. Based on both visual quality and quantitative metric analysis, it is observed that M3IF‐(SWT‐TVC) outperforms all the methods used for comparison.