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Medical Images Fusion with Patch Based Structure Tensor

机译:基于补丁的结构张量的医学图像融合

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摘要

Nowadays medical imaging has played an important role in clinical use, which provide important clues for medical diagnosis. In medical image fusion, the extraction of some fine details and description is critical. To solve this problem, a modified structure tensor by considering similarity between two patches is proposed. The patch based filter can suppress noise and add the robustness of the eigen-values of the structure tensor by allowing the use of more information of far away pixels. After defining the new structure tensor, we apply it into medical image fusion with a multi-resolution wavelet theory. The features are extracted and described by the eigen-values of two multi-modality source data. To test the performance of the proposed scheme, the CT and MR images are used as input source images for medical image fusion. The experimental results show that the proposed method can produce better results compared to some related approaches.
机译:如今,医学成像在临床应用中已经发挥了重要作用,为医学诊断提供了重要线索。在医学图像融合中,提取一些细节和描述至关重要。为了解决这个问题,提出了一种通过考虑两个面片之间的相似性来改进结构张量的方法。基于面片的滤波器可以通过允许使用更多像素信息来抑制噪声并增加结构张量本征值的鲁棒性。在定义了新的结构张量之后,我们使用多分辨率小波理论将其应用于医学图像融合。通过两个多模态源数据的特征值提取和描述特征。为了测试所提出方案的性能,将CT和MR图像用作医学图像融合的输入源图像。实验结果表明,与某些相关方法相比,该方法可以产生更好的结果。

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