In medical image processing, image fusion is the process of combining complementary information from different or multimodality images to obtain an informative fused image in order to improve clinical diagnostic accuracy. In this paper, we propose a two‐stage fusion framework for computed tomography (CT) and magnetic resonance (MR) images. First, the intensity and geometric structure features in both CT and MR images are extracted by the saliency detection method and structure tensor, respectively, and an initial fused image is obtained. Then, the initial fused image is optimized by a variational model which contains a fidelity term and a regularization term. The fidelity term is to retain the intensity of the initial fused image, and the regularization term is to constrain the gradient information of the fused image to approximate the MR image. The primal‐dual algorithm is proposed to solve the variational problem. The proposed method is applied on five pairs of clinical medical CT and MR‐T1\MR‐T2 images, and the comparison metrics SF, MI, , , and VIFF are calculated for assessment. Compared with seven state‐of‐the‐art methods, the proposed method shows a comprehensive advantage in preserving the salient intensity features, as well as texture structure information, not only in visual effects but also in objective assessments.

中文翻译：

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一种有效的融合算法，结合了特征提取和变异优化的CT和MR图像。

在医学图像处理中，图像融合是组合来自不同或多模态图像的补充信息以获得信息丰富的融合图像以提高临床诊断准确性的过程。在本文中，我们为计算机断层扫描（CT）和磁共振（MR）图像提出了两阶段融合框架。首先，分别通过显着性检测方法和结构张量提取CT和MR图像中的强度和几何结构特征，并获得初始融合图像。然后，通过包含保真度项和正则项的变分模型来优化初始融合图像。保真度项将保留初始融合图像的强度，正则项将约束融合图像的梯度信息以逼近MR图像。提出了原始对偶算法来解决变分问题。该方法适用于五对临床医学CT和MR-T1 \ MR-T2图像，以及比较指标SF，MI，，，和VIFF进行计算以进行评估。与七种最新方法相比，所提出的方法在保留显着强度特征以及纹理结构信息方面不仅在视觉效果上而且在客观评估上都显示出综合优势。