Multi-modal Image Fusion Based on Weight Local Features and Novel Sum-Modified-Laplacian in Non-subsampled Shearlet Transform Domain

Abstract

Multi-modal medical image fusion plays a significant role in clinical applications like noninvasive diagnosis and image-guided surgery. However, designing an efficient image fusion technique is still a challenging task. In this paper, we propose an improved multi-modal medical image fusion method to enhance the visual quality and contrast of the fused image. To achieve this work, the registered source images are firstly decomposed into low-frequency (LF) and several high-frequency (HF) sub-images via non-subsampled shearlet transform (NSST). Afterward, LF sub-images are combined using the proposed weight local features fusion rule based on local energy and standard deviation, while HF sub-images are fused based on the novel sum-modified-laplacien (NSML) technique. Finally, inversed NSST is applied to reconstruct the fused image. Furthermore, the proposed method is extended to color multi-modal image fusion that effectively restrains color distortion and enhances spatial and spectral resolutions. To evaluate the performance, various experiments conducted on different datasets of gray-scale and color images. Experimental results show that the proposed scheme achieves better performance than other state-of-art proposed algorithms in both visual effects and objective criteria.