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.