Multirate systems and filter banks
Multirate systems and filter banks
Multisensor image fusion using the wavelet transform
Graphical Models and Image Processing
SUSAN—A New Approach to Low Level Image Processing
International Journal of Computer Vision
A region-based multi-sensor image fusion scheme using pulse-coupled neural network
Pattern Recognition Letters
Medical image fusion using m-PCNN
Information Fusion
Contrast enhancement in emission tomography by way of synergistic PET/CT image combination
Computer Methods and Programs in Biomedicine
Multifocus image fusion using the nonsubsampled contourlet transform
Signal Processing
Image fusion based on a new contourlet packet
Information Fusion
MRI and PET image fusion by combining IHS and retina-inspired models
Information Fusion
Real time human visual system based framework for image fusion
ICISP'10 Proceedings of the 4th international conference on Image and signal processing
Biological image fusion using a NSCT based variable-weight method
Information Fusion
Fuzzy image fusion based on modified Self-Generating Neural Network
Expert Systems with Applications: An International Journal
Pixel-level image fusion with simultaneous orthogonal matching pursuit
Information Fusion
IEEE Transactions on Pattern Analysis and Machine Intelligence
Ultrasonic liver tissue characterization by feature fusion
Expert Systems with Applications: An International Journal
Gradient-based multiresolution image fusion
IEEE Transactions on Image Processing
Hi-index | 12.05 |
Multi-modal medical image fusion, as a powerful tool for the clinical applications, has developed with the advent of various imaging modalities in medical imaging. The main motivation is to capture most relevant information from sources into a single output, which plays an important role in medical diagnosis. In this paper, a novel framework for medical image fusion based on framelet transform is proposed considering the characteristics of human visual system (HVS). The core idea behind the proposed framework is to decompose all source images by the framelet transform. Two different HVS inspired fusion rules are proposed for combining the low- and high-frequency coefficients respectively. The former is based on the visibility measure, and the latter is based on the texture information. Finally, the fused image is constructed by the inverse framelet transform with all composite coefficients. Experimental results highlight the expediency and suitability of the proposed framework. The efficiency of the proposed method is demonstrated by the different experiments on different multi-modal medical images. Further, the enhanced performance of the proposed framework is understood from the comparison with existing algorithms.