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Voxel similarity measures for 3-D serial MR brain image registration

机译:3-D串行MR脑图像配准的体素相似性度量

摘要

We have evaluated eight different similarity measures used for rigid body registration of serial magnetic resonance (MR) brain scans. To assess their accuracy we used 33 clinical three-dimensional (3-D) serial MR images, with deformable extradural tissue excluded by manual segmentation and simulated 3-D MR images with added intensity distortion. For each measure we determined the consistency of registration transformations for both sets of segmented and unsegmented data. We have shown that of the eight measures tested, the ones based on joint entropy produced the best consistency. In particular, these measures seemed to be least sensitive to the presence of extradural tissue. For these data the difference in accuracy of these joint entropy measures, with or without brain segmentation, was within the threshold of visually detectable change in the difference images.
机译:我们已经评估了八种不同的相似性度量值,用于串行磁共振(MR)脑部扫描的刚体配准。为了评估其准确性,我们使用了33幅临床三维(3-D)串联MR图像,通过手动分割排除了可变形的硬膜外组织,并模拟了具有增加强度失真的3-D MR图像。对于每种度量,我们确定了分段和非分段数据集的套准转换的一致性。我们已经表明,在测试的八种方法中,基于联合熵的方法产生了最佳的一致性。特别地,这些措施似乎对硬膜外组织的存在最不敏感。对于这些数据,无论有无脑分割,这些联合熵测度的准确性差异都在差异图像中视觉上可察觉的变化的阈值之内。

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