Multimodality Medical Image Fusion Using Clustered Dictionary Learning in Non-Subsampled Shearlet Transform

Abstract

Imaging data fusion is becoming a bottleneck in clinical applications and translational research in medical imaging. This study aims to incorporate a novel multimodality medical image fusion technique into the shearlet domain. The proposed method uses the non-subsampled shearlet transform (NSST) to extract both low- and high-frequency image components. A novel approach is proposed for fusing low-frequency components using a modified sum-modified Laplacian (MSML)-based clustered dictionary learning technique. In the NSST domain, directed contrast can be used to fuse high-frequency coefficients. Using the inverse NSST method, a multimodal medical image is obtained. Compared to state-of-the-art fusion techniques, the proposed method provides superior edge preservation. According to performance metrics, the proposed method is shown to be approximately 10% better than existing methods in terms of standard deviation, mutual information, etc. Additionally, the proposed method produces excellent visual results regarding edge preservation, texture preservation, and more information.

TDFusion: When Tensor Decomposition Meets Medical Image Fusion in the Nonsubsampled Shearlet Transform Domain

“…In recent years, many well-known approaches based on machine learning, deep learning, or some other methods for brain tumor detection and identification have emerged. Diwakar et al [ 23 ] used the non-subsampling shearlet transform (NSST) to extract low- and high-frequency image components in multimodal medical images and proposed a new method for low frequency component fusion based on an improved and modified Laplacian (MSML) clustering dictionary learning technique. In the NSST domain, directional contrast can be used to fuse high-frequency coefficients while using the inverse NSST method to obtain multimodal medical images.…”

Advanced AI-driven image fusion techniques in lung cancer diagnostics: systematic review and meta-analysis for precisionmedicine

“…(2022); Qi and Su (2022) have introduced innovative fusion algorithms, significantly elevating the diagnostic process. For instance, clustered dictionary learning within the non-subsampled shearlet transform domain introduced by Diwakar et al. (2023) has shown promising edge preservation in multimodality medical imaging, surpassing traditional methods in both contrast and information extraction.…”