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For proper homoeopathic identification of the medical image, image fusion has been proposed as a mandatory solution to obtain high-spectral and high-spectral spatial data. This article presents a complete fusion system for several types of medical images according to their multi-resolution, multi-scale transforms and the Modified Central Force Optimization (MCFO) technique. Four main techniques have been proposed for this purpose; Optimized Discrete Wavelet and Dual-Tree based fusion techniques as a multi-resolution transform. Besides, the optimized Non-Sub-Sampled Contourlet and Non-Sub-Sampled Shearlet as multi-scale fusion techniques. The perfect matching between input images and minimum artifacts after image registration can be achieved through four stages in the proposed fusion algorithms. First, the input medical image is initially decomposed into their coefficients, and the MCFO method establishes the optimal gain parameter values of the resulted coefficients. Finally, the adaptive histogram equalization and the histogram matching are applied for higher clearness and better visualization of information details. The proposed algorithms are evaluated using various datasets for different medical and surveillance applications through some quality metrics. The Experimental test outcomes indicate that the proposed fusion algorithms achieve good performance with high image quality and appreciated estimation metrics principles. Moreover, it provides better image visualization and minimum processing time, which helps diagnose diseases. [ABSTRACT FROM AUTHOR]

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, Qab/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. [ABSTRACT FROM AUTHOR]

Highlights • Related works. • Modified Central Force Optimization (MCFO). • The proposed optimized medical image fusion system. • Local contrast enhancement techniques. • Simulation results and comparisons. 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. [ABSTRACT FROM AUTHOR]

Infrared, visible, and medical image fusion systems are mandatory solutions for obtaining more spatial and more spectral information in a single image for efficient object detection and medical diagnosis applications. In this paper, an optimized fusion system for multi-modality images based on the Non-sub-Sampled Shearlet Transform (NSST) with Modified Central Force Optimization (MCFO), histogram matching, and local contrast enhancement is presented. The proposed multi-modality image fusion system consists of four stages. The first stage is the image registration and then histogram matching of one of the images to the other to allow the same dynamic range for both images to minimize the fusion artifacts. The NSST is used after that to decompose the images to be fused into their coefficients. The NSST provides a better sparse image representation of highly localized coefficients, anisotropic directionality, and reduced pseudo-Gibbs artifacts. After that, 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 system is subjectively and objectively evaluated with different fusion quality metrics including average gradient, local contrast, standard deviation, edge intensity, entropy, Peak Signal-to-Noise Ratio (PSNR), and Qab/f. Real infrared, visible, and medical datasets of different modalities are used to test the proposed system. Experimental results demonstrate that the proposed system achieves a superior performance with higher image quality, higher evaluation metrics values, and much more details in images. These characteristics help for more accuracy in applications such as object detection and medical diagnosis. [ABSTRACT FROM AUTHOR]