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Image Processing is an art to get an enriched image or it can be used to retrieve information. This image processing methods are used in medical field also. Numerous modalities like Magnetic Resonance Imaging (MRI), Positron Emission Tomography (PET), and Computed Tomography (CT) etc. are used to analyze and diagnose diseases.Pixel-level image fusion is a combination of several images collected from various inputs and gives more information than any other input messages. Pixel-level image fusion shows a vital role in medical imaging. In this paper, pixel-level image fusionsmethods are survived and review the fusion quality measures are being used. Finally this surveycomplete with different kinds of image fusion methods proposed and still there are so many imminent ways in image fusion applications. Hence image fusions fields are pointedly develop in the forthcoming years.

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Medical image fusion (MIF) is essential in clinical domain that integrates the multi-modal medical features to a unique frame known as fused image which finds utility in diagnosis process. Scaling based approaches are the commonly used multimodal MIF model where the generalized scaling has a stationary scale value selection that enhances the fusion quality Discrete Wavelet Transform (db4)-based approaches give a maximum amount of approximation in multi-modal medical image fusion, while using less edge features. For generating efficient edge features, Laplacian filtering (LF) approach is employed. This paper introduces an optimized Laplacian Wavelet Mask (OLWM) based fusion model for multi-modal MIF using Variable Weight Grey Wolf Optimization (VW-GWO). An enhanced GWO algorithm with variable weights (VW-GWO) is faced with the idea of using the social hierarchy of the grey wolves to locate the searching positions. Besides, to minimize the possibility of trapping into local optima, an efficient parameter control mechanism is employed. The VW-GWO algorithm has the capability to choose the control variables of the GWO algorithm in an automated way. A set of medical images, including MR-SPECT, MR-PET, MR-CT and MR: T1–T2 of brain scans, validates the proposed VW-GWO algorithm. The simulation outcome showed that the effectiveness of the VW-GWO algorithm seems to be much higher over the compared methods under various dimensions.