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首页> 外文期刊>Journal of computational and theoretical nanoscience >A Multi-Similarity Fusion Feature-Point Matching Approach for Measuring Internal Deformation Fields Using Magnetic Resonance Volumetric Images of Biological Tissues
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A Multi-Similarity Fusion Feature-Point Matching Approach for Measuring Internal Deformation Fields Using Magnetic Resonance Volumetric Images of Biological Tissues

机译：一种使用生物组织的磁共振体积图像测量内部形变场的多相似性融合特征点匹配方法

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摘要

Various feature-point matching approaches have been reported in computer vision. However, few feature-point matching approaches employed on images from non-rigid and non-uniform biological tissues have been reported. The present study investigates the interior deformation field measurement of complex biological tissues from three-dimensional magnetic resonance (MR) volumetric images. To improve the reliability of the matching, we propose the fusion of three different similarity measurement methods. The basic algorithm of the three different similarity measurement methods and their fusion to generate an integration method to improve the results are discussed. The validity of the proposed approach was tested by applying it to actual MR volumetric images that captured the calf of a volunteer. The results indicate that the fusion method is more effective than the single feature-point matching approach.
机译：在计算机视觉中已经报道了各种特征点匹配方法。然而，已经报道了很少有特征点匹配方法用于来自非刚性和非均匀生物组织的图像。本研究从三维磁共振（MR）体积图像研究复杂生物组织的内部变形场测量。为了提高匹配的可靠性，我们提出了三种不同相似度测量方法的融合。讨论了三种不同相似度测量方法的基本算法以及它们的融合以生成一种集成方法以改善结果。通过将其应用于捕获志愿者小腿的实际MR体积图像来测试该方法的有效性。结果表明，融合方法比单特征点匹配方法更有效。

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