Improving performance of medical image fusion using histogram, dictionary learning and sparse representation

Abstract

Medical image fusion has attracted much attention in recent years, which aims to fuse different medical images into a more informative and clearer one. The fused image is able to help doctors to diagnose diseases rapidly and effectively. Among numerous fusion methods, sparse-representation-based image fusion is a new concept that has emerged over the past several years. However, the high-frequency components of low-resolution and the high-frequency components of source images are obtained equally, and sparse coefficients are solved by a minimization problem. As a result, it ignores the correlation between high-frequency components of low-resolution and the high-frequency components of source images, and solutions to the L0-norm minimization problem. To address these issues, we propose a new image fusion method based on histogram similarity and multi-view weighted sparse representation. By introducing a histogram similarity, different weights are assigned to the high-frequency components of low-resolution and the high-frequency components of source images to efficiently harness the complementary information. In addition, sparse coefficients solved by the L1-norm minimization problem are more accurate. This technique is further incorporated into medical image fusion. Experimental results demonstrate that the proposed method achieves state-of-the-art performance in terms of both visual quality and quantitative evaluation metrics.