Chongqing Key Laboratory of Image Cognition, Chongqing University of Posts and Telecommunications, Chongqing, 400065, China.
Chongqing Key Laboratory of Image Cognition, Chongqing University of Posts and Telecommunications, Chongqing, 400065, China.
Comput Biol Med. 2021 Feb;129:104179. doi: 10.1016/j.compbiomed.2020.104179. Epub 2020 Dec 17.
The aim of medical image fusion technology is to synthesize multiple-image information to assist doctors in making scientific decisions. Existing studies have focused on preserving image details while avoiding halo artifacts and color distortions. This paper proposes a novel medical image fusion algorithm based on this research objective. First, the input image is decomposed into structure, texture, and local mean brightness layers using a hybrid three-layer decomposition model that can fully extract the features of the original images without the introduction of artifacts. Secondly, the nuclear norm of the patches, which are obtained using a sliding window, are calculated to construct the weight maps of the structure and texture layers. The weight map of the local mean brightness layer is constructed by calculating the local energy. Finally, remapping functions are applied to enhance each fusion layer, which reconstructs the final fusion image with the inverse operation of decomposition. Subjective and objective experiments confirm that the proposed algorithm has a distinct advantage compared with other state-of-the-art algorithms.
医学图像融合技术的目的是综合多种图像信息，辅助医生做出科学决策。现有研究主要集中在保留图像细节的同时避免晕影伪影和颜色失真。本文基于这一研究目标，提出了一种新的医学图像融合算法。首先，使用混合三层分解模型将输入图像分解为结构、纹理和局部平均亮度层，该模型可以充分提取原始图像的特征，而不会引入伪影。其次，通过滑动窗口计算斑块的核范数，构建结构和纹理层的权重图。通过计算局部能量构建局部平均亮度层的权重图。最后，应用重映射函数增强每个融合层，通过分解的逆操作重构最终的融合图像。主观和客观实验证实，与其他最先进的算法相比，所提出的算法具有明显的优势。
