Medical Image Fusion Based on Weighted Least Square Optimization and Deep Learning Algorithm

Abstract

Recently, medical image processing has become a hot area of research, especially with the rapid development in technology and instrumentation, and that’s because of its effective role in the health sector. So that it becomes a very active research tool. This research introduces an image fusion algorithm that utilizes a deep learning model to produce only one medical fused image that includes all the traits from the medical source images. Firstly, the source images are separated into detailed content and base parts using the Gaussian and rolling guidance filters (RGF). Secondly, by the weighted averaging strategy, the base parts are fused. For the detail content, to quote traits of multi-layer which employ weighted average strategy to produce the fused detail content several candidates, the deep learning network is utilized. The max selection technique is employed to gain the last detailed content as soon as these candidates are gotten. Eventually, by uniting the fused detail and base layers, the fused image will be recreated. The experimental outcomes show that this algorithm can accomplish better results by comparing the other fusion methods in both thematic assessment and visual quality.