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Transformation optimization and image blending for 3D liver ultrasound series stitching

机译:3D肝脏超声系列拼接的变换优化和图像融合

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We propose a consistent ultrasound volume stitching framework, with the intention to produce a volume with higher imago quality and extended field-of-view in this work. Directly using pair-wise registrations for stitching may lead to geometric errors. Therefore, we propose an approach to improve the image alignment by optimizing a consistency metric over multiple pairwise registrations. In the optimization, we utilize transformed points to effectively compute a distance between rigid transformations. The method has been evaluated on synthetic, phantom and clinical data. The results indicate that our transformation optimization method is effective and our stitching framework has a good geometric precision. Also, the compound images have been demonstrated to have improved CNR values.
机译:我们提出了一个一致的超声体积缝合框架,目的是在这项工作中生产出具有更高图像质量和更大视野的体积。直接使用成对套准进行拼接可能会导致几何错误。因此,我们提出了一种通过在多个成对配准上优化一致性度量来改善图像对齐的方法。在优化中,我们利用变换点来有效地计算刚性变换之间的距离。该方法已经过综合,幻像和临床数据的评估。结果表明,该变换优化方法是有效的,拼接框架具有良好的几何精度。而且,已经证明复合图像具有改善的CNR值。

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