Multimodal Medical Image Fusion Based on Weighted Local Energy Matching Measurement and Improved Spatial Frequency

Abstract

Multimodal medical image fusion (MMIF) technology can effectively improve the efficiency and accuracy of doctors in clinical diagnosis and treatment by combining different modes of medical images into a medical image with rich information. To achieve an optimal balance between computational loss and fusion quality, this article presents a new MMIF method based on weighted local energy matching measurement (WLEMM) and improved spatial frequency (SF). First, the latent low-rank representation (LatLRR) is employed to learn a decomposition matrix that is applied to decompose a source image into its base part and saliency part. Then, in order to adaptively select a fusion strategy for the base part, we propose a novel fusion rule, namely WLEMM, which is constructed by calculating the matching degree between the corresponding pixels of the base parts. Meanwhile, after considering the characteristics of the main diagonal SF and the secondary diagonal SF, a fusion strategy based on improved SF and L2 norm is designed to merge saliency parts. Finally, the fused image is obtained by combining the fused base part and saliency part. Various experiments on multiple groups of medical images demonstrate that the proposed method has superior performance compared with the state-of-the-art fusion methods, in subjective visual and objective index evaluations. Furthermore, the research work of this article is helpful for further detection and segmentation of medical images.