Medical Image Fusion Using Integrated Guided Nonlinear Anisotropic Filtering with Image Statistics

Abstract

An image that is highly informative is produced by unifying the information from couple of or numerous source images which is referred as image fusion. It has been employing in most of the applications in the medical field like detecting of tumours, treating Alzheimer's, surgery of brain with the assistance of computer and some other clinical diagnosis. Successful diagnosis of specific diseases necessitates the enhancement in the exactness of fusion algorithms. For examining the body of human, the images obtained from magnetic resonance imaging (MRI) and computed tomography (CT) plays a vital role. Channelizing the maximum info from the source images to the fused image with understate loss of info that must mitigate the presence of artifacts in the fused outcome is the basic idea of any fusion methodology. In this context, a novel medical image fusion approach is implemented, that utilizes integrated guided and nonlinear anisotropic (IGNLA) filtering with image statistics. This approach upholds the info of texture in the fused images more efficaciously. In addition, proposed medical image fusion is extended for color images and applied to MR-Gad, MR-T2 and SPECT-Tc images. Extensive simulation results of proposed medical image fusion are compared with traditional and recent image fusion algorithms and disclose the superiority of proposed approach with respect to image quality metrics.