Sparse Features with Fast Guided Filtering for Medical Image Fusion

Abstract

In medical image fusion, partially focused images are combined into a completely informative in focus image of the same scene, helping the clinical applicability of medical image for assessment on sports injuries diagnosis and rehabilitation. However, many state-of-the-art fusion methods seldom consider structural differences between guidance and input images, so not all the significant features of the source images can be well preserved for a completely informative focused image. In this paper, a fusion method via fast guided filtering with sparse features is proposed. The source images are decomposed into base and detail layers using an average filter. They are also split into low-rank and sparse parts by solving convex programs. Saliency maps are constructed based on the sparse features using low pass and high pass filters. Fast guided filtering is used to optimize saliency maps for constructing weighted maps of the base and detail layers, and for maintaining the spatial consistency between the source images and the layers. The fused base and detail layers are integrated to construct the final fused image. Experiments on nine pair of medical images demonstrate that the proposed method achieved better medical images compared to other reported methods in terms of both subjective and objective evaluations.