Medical Image Fusion Using SKWGF and SWF in Framelet Transform Domain

Abstract

Accurately localizing and describing patients’ lesions has long been considered a crucial aspect of clinical diagnosis. The fusion of multimodal medical images provides a feasible solution to the above problem. Unfortunately, the trade-off between the fusion performance and heavy computation overhead remains a challenge. In this paper, a novel and effective fusion method for multimodal medical images is proposed. Firstly, framelet transform (FT) is introduced to decompose the source images into a series of low and high frequency sub-images. Next, we utilize the benefits of both steering kernel weighted guided filtering and side window filtering to successfully fuse sub-images. Finally, the inverse FT is employed to reconstruct the final fused image. To verify the effectiveness of the proposed fusion method, we fused several pairs of medical images covering different modalities in simulation experiments. The experimental results demonstrate that the proposed method yields better performance than current representative ones in terms of both visual quality and quantitative evaluation.