In the current era of technological development, medical imaging plays an important role in many applications of medical diagnosis and therapy. In this regard, medical image fusion could be a powerful tool to combine multi-modal images by using image processing techniques. But, conventional approaches failed to provide the effective image quality assessments and robustness of fused image. To overcome these drawbacks, in this work three-stage multiscale decomposition (TSMSD) using pulse-coupled neural networks with adaptive arguments (PCNN-AA) approach is proposed for multi-modal medical image fusion. Initially, nonsubsampled shearlet transform (NSST) is applied onto the source images to decompose them into low frequency and high frequency bands. Then, low frequency bands of both the source images are fused using nonlinear anisotropic filtering with discrete Karhunen–Loeve transform (NLAF-DKLT) methodology. Next, high frequency bands obtained from NSST are fused using PCNN-AA approach. Now, fused low frequency and high frequency bands are reconstructed using NSST reconstruction. Finally, band fusion rule algorithm with pyramid reconstruction is applied to get final fused medical image. Extensive simulation outcome discloses the superiority of proposed TSMSD using PCNN-AA approach as compared to state-of-the-art medical image fusion methods in terms of fusion quality metrics such as entropy (E), mutual information (MI), mean (M), standard deviation (STD), correlation coefficient (CC) and computational complexity.
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.
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