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首页> 外文期刊>Journal of Medical Imaging and Health Informatics >Evaluation of Four Similarity Measures for 2D/3D Registration in Image-Guided Intervention
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Evaluation of Four Similarity Measures for 2D/3D Registration in Image-Guided Intervention

机译：图像引导干预中2D / 3D配准的四个相似性度量的评估

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2D/3D medical image registration in image-guided intervention is crucial to assist the clinician to establish the space corresponding relationship between image information and patients' anatomy, which can be quantified by a similarity measure. Among similarity measures, mutual information and its derivatives, were used widely for image registration, and showed significantly differences in the performances of registrations. However, the comparison of their registration performances has not been studied quantitatively yet. Therefore, in this paper, four similarity measures were evaluated for 2D/3D rigid registrations, which are mutual information (MI) and its three derivatives (distance coefficient mutual information (DCMI), distance weighted mutual information (DWMI), gradient weighted mutual information (GWMI)). They were applied to implement registrations based on porcine skull phantom datasets from the Medical University of Vienna, and were evaluated through the mean target registration errors (mTRE) for the registrations. The results demonstrated that the performance of DCMI was the most accurate and robust, and MI was the least effective of the four similarity measures. Moreover, due to the presence of a great amount of soft tissues, GWMI also had the low performance with its mean of mTRE even greater than that by MI, which suggested that intensity gradients were not always having a positive impact for 2D/3D rigid registration when involving a great amount of soft tissues. Between DCMI and DWMI, there were a significant difference in terms of accuracy and robustness, despite using the same image information for them, which means that the construction of an ideal measure should consider not only the image information to be involved but also the construction way of these information.
机译：图像引导干预中的2D / 3D医学图像配准对于协助临床医生建立图像信息与患者解剖结构之间的空间对应关系至关重要，可以通过相似性度量对其进行量化。在相似性度量中，互信息及其衍生物被广泛用于图像配准，并且在配准性能方面显示出显着差异。但是，尚未对它们的注册性能进行比较的定量研究。因此，在本文中，对2D / 3D刚性配准评估了四个相似性度量，分别是互信息（MI）及其三个导数（距离系数互信息（DCMI），距离加权互信息（DWMI），梯度加权互信息（GWMI））。他们被用于基于维也纳医科大学的猪头骨幻象数据集实施注册，并通过注册的平均目标注册错误（mTRE）进行了评估。结果表明，在四种相似性度量中，DCMI的性能最准确，最可靠，而MI的效果最差。此外，由于存在大量的软组织，GWMI的mTRE平均值也比MI差，因此其性能较低，这表明强度梯度并不总是对2D / 3D刚性配准产生积极影响当涉及大量的软组织时。尽管DCMI和DWMI使用相同的图像信息，但它们在准确性和鲁棒性方面存在显着差异，这意味着理想措施的构建不仅应考虑涉及的图像信息，而且还应考虑构建方式这些信息。

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