An efficient DT-CWT medical image fusion system based on modified central force optimization and histogram matching

Abstract

This paper presents an efficient medical image fusion system based on Dual-Tree Complex Wavelet Transform (DT-CWT) and the Modified Central Force Optimization (MCFO) technique. The first step in the proposed system is the histogram matching of one of the images to the other to allow the same dynamic range for both images. The DT-CWT is used after that to decompose the images to be fused into their coefficients. The MCFO technique is used to determine the optimum decomposition level and the optimum gain parameters for the best fusion of coefficients based on certain constraints. Finally, an additional contrast enhancement process is applied on the fused image to enhance its visual quality and reinforce details. A comparative study between the traditional spatial and transform domain fusion techniques and the proposed optimized DT-CWT fusion system is presented. The proposed fusion system is subjectively and objectively tested and evaluated with different fusion quality metrics. Simulation results demonstrate that the proposed optimized DT-CWT medical image fusion system based on MCFO and histogram matching achieves a superior performance with better image quality, much more details. These characteristics help in a more accurate medical diagnosis.