Multimodal Sensor Medical Image Fusion Based on Local Difference in Non-Subsampled Domain

Medical imaging sensors, such as positron emission tomography and single-photon emission computed tomography, can provide rich information, but each has its inherent drawbacks. In this scenario, multimodal sensor medical image fusion becomes an effective solution. The chief objective of medical imaging is to extract as much preponderant and complementary information as possible from the source into a single output that can play a critical role in medical diagnosis and clinical operations. In this paper, a novel fusion method is presented for multimodal sensor medical images, based on local difference (LD) in non-subsampled domain. In this method, the source medical images are first decomposed into low-frequency and high-frequency subimages, via non-subsampled schemes. Then, the coefficients of sub-bands are fused by an operator, called LD. The final fused image is reconstructed, via the inverse non-subsampled schemes, with all composite coefficients. The proposed fusion method was applied in several clinical studies, and the results show that it is a much more straightforward and effective method than some of the state-of-the-art methods, in terms of both subjective visual performance and objective evaluation results. Also, the performance of the proposed method was compared with that of two non-subsampled schemes, namely, non-subsampled contourlet transform and non-subsampled shearlet transform.