Proceedings Article | 1 June 2020
KEYWORDS: Image fusion, Discrete wavelet transforms, Neural networks, Medical imaging, Tissues, Evolutionary algorithms, Wavelets, Fuzzy logic, Tumors, Magnetic resonance imaging
With the rapid development of computer technology and the advent of the information age, diverse medical imaging devices are emerging. However, limited by imaging principles, single-mode images have their own advantages and disadvantages, and it is difficult to fully express all practical information, causing the limitations of diagnosis. Accordingly, medical image fusion is inevitable trend which could integrate or highlight the complementary information, achieve enhanced image quality, reduce redundancy, and provide a reliable diagnosis. In the past, there were many methods that were proposed, but the effect was largely dependent on the experimental data. Based on this, in this study, we proposed a new image fusion method based on discrete wavelet transform (DWT) and fuzzy radial basis function neural network (FRBFNN). First, we analyzed the details or feature information of two images to be processed by DWT. Here, we used a 2- level decomposition, so that each image was decomposed into 7 parts including high frequency sub-bands and low frequency sub-bands. Subsequently, for the parts of the same position of the two images, we substituted them to the proposed FRBFNN. So, with the operation of these seven neural networks, we obtained seven fused parts in turn. Finally, through the inverse wavelet transform, we could get the final fused image. For the training method of neural network, we adopted the combination of error backpropagation algorithm and gravity search algorithm. The final experimental results demonstrated that our method performed significantly better than other algorithms.
