Abstract
“Self-similarity” is a common characteristic of medical images. That is, small-scale features often appear in multiple locations in the image frequently. Therefore, the global search for similar pixels helps to infer the pixel value of a certain location, which can be used for the extraction of image details. In this paper, a two-stage image decomposition framework (TS-PLNSS) is proposed. It combines the pixel-level nonlocal self-similarity prior and pixel intensity attributes of the image. First, the source image is adaptively decomposed into three scales using different thresholds: texture layer, structure layer, and base layer. Then, different fusion strategies are adopted according to the characteristics of different layers. Among them, an optimization function that integrates structural information is designed to preserve the salient information of the source image to the maximum extent. Finally, the fused medical image is obtained through image reconstruction. The proposed method is compared with seven state-of-the-art multimodal medical image fusion methods to verify its effectiveness. The dataset consists of 110 image pairs, including CT/SPECT, MRI/PET, MRI/SPECT, MRI/CT, and GFP/PC. The subjective and objective evaluation results show that the TS-PLNSS method has better ability to distinguish and retain important information and texture details without distortion.
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Zhu, R., Li, X., Wang, Y., Zhang, X. (2022). Medical Image Fusion Based on Pixel-Level Nonlocal Self-similarity Prior and Optimization. In: Bhattacharya, A., et al. Database Systems for Advanced Applications. DASFAA 2022. Lecture Notes in Computer Science, vol 13247. Springer, Cham. https://doi.org/10.1007/978-3-031-00129-1_18
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