Multi-modal Medical Neurological Image Fusion using Wavelet Pooled Edge
  Preserving Autoencoder
Manisha Das Deep Gupta Petia Radeva Ashwini M Bakde
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.