Optimal multi-scale geometric fusion based on non-subsampled contourlet transform and modified central force optimization

摘要

In the current era of technological development, medical imaging plays an important part in several applications of medical diagnosis and therapy. This requires more precise images with much more details and information for correct medical diagnosis and therapy. Medical image fusion is one of the solutions for obtaining much spatial and spectral information in a single image. This article presents an optimization-based contourlet image fusion approach in addition to a comparative study for the performance of both multi-resolution and multi-scale geometric effects on fusion quality. An optimized multi-scale fusion technique based on the Non-Subsampled Contourlet Transform (NSCT) using the Modified Central Force Optimization (MCFO) and local contrast enhancement techniques is presented. The first step in the proposed fusion approach is the histogram matching of one of the images to the other to allow the same dynamic range for both images. The NSCT 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. The proposed fusion framework is subjectively and objectively evaluated with different fusion quality metrics including average gradient, local contrast, standard deviation (STD), edge intensity, entropy, peak signal-to-noise ratio, Q(ab/f), and processing time. Experimental results demonstrate that the proposed optimized NSCT medical image fusion approach based on the MCFO and histogram matching achieves a superior performance with higher image quality, average gradient, edge intensity, STD, better local contrast and entropy, a good quality factor, and much more details in images. These characteristics help for more accurate medical diagnosis in different medical applications.