Medical image fusion integrates the complementary diagnostic information of the source image modalities for improved visualization and analysis of underlying anomalies. Recently, deep learning-based models have excelled the conventional fusion methods by executing feature extraction, feature selection, and feature fusion tasks, simultaneously. However, most of the existing convolutional neural network (CNN) architectures use conventional pooling or strided convolutional strategies to downsample the feature maps. It causes the blurring or loss of important diagnostic information and edge details available in the source images and dilutes the efficacy of the feature extraction process. Therefore, this paper presents an end-to-end unsupervised fusion model for multimodal medical images based on an edge-preserving dense autoencoder network. In the proposed model, feature extraction is improved by using wavelet decomposition-based attention pooling of feature maps. This helps in preserving the fine edge detail information present in both the source images and enhances the visual perception of fused images. Further, the proposed model is trained on a variety of medical image pairs which helps in capturing the intensity distributions of the source images and preserves the diagnostic information effectively. Substantial experiments are conducted which demonstrate that the proposed method provides improved visual and quantitative results as compared to the other state-of-the-art fusion methods.

翻译：医学图像融合通过整合源图像模态的互补诊断信息，改善潜在异常的可视化与分析效果。近年来，基于深度学习的模型通过同步执行特征提取、特征选择与特征融合任务，显著超越了传统融合方法。然而现有卷积神经网络架构普遍采用常规池化或步长卷积策略对特征图进行下采样，这导致源图像中的关键诊断信息与边缘细节出现模糊或丢失，弱化了特征提取过程的有效性。为此，本文提出一种基于边缘保持密集自编码网络的端到端无监督多模态医学图像融合模型。该模型通过采用基于小波分解的注意力池化策略改进特征提取，既保留了源图像中精细的边缘细节信息，又增强了融合图像的视觉感知效果。此外，所提模型在多样化医学图像对上进行训练，有效捕获源图像的强度分布特性并维持诊断信息的完整性。大量实验表明，与现有先进融合方法相比，本方法在视觉与量化指标上均取得更优结果。