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Medical image registration based on watershed transform from greyscale marker and multi-scale parameter search

机译:基于灰度标记的分水岭变换和多尺度参数搜索的医学图像配准

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We propose an automatic 3D medical image registration method that combines the watershed transform from greyscale marker, to effectively reduce the number of key points needed for registration, with a robust optimisation algorithm, called multi-scale parameter search (MSPS), to quickly estimate the mapping function. We evaluate it for rigid and intra-subject registration of pre- and post-surgery MR-T1 images of the brain. The visual analysis of its effectiveness is facilitated by a colour-coding scheme. Extensive experiments show that our approach is very accurate, robust to noise and provides 3D registration in less than 40 s, with no multi-resolution image schemes needed. We also evaluate MSPS on a testbed of 12 optimisation benchmark problems, in comparison with well-known optimisers, such as particle swarm optimiser, simulated annealing and differential evolution, showing that it can also be explored in other applications.
机译:我们提出了一种自动3D医学图像配准方法,该方法结合了灰度标记的分水岭变换,以有效减少配准所需的关键点数量,并使用一种称为多尺度参数搜索(MSPS)的强大优化算法来快速估算映射功能。我们评估它的大脑术前和术后MR-T1图像的刚性和对象内配准。颜色编码方案有助于对其效果进行视觉分析。大量的实验表明,我们的方法非常准确,抗噪声,并且可以在不到40秒的时间内提供3D配准,而无需多分辨率图像方案。与众所周知的优化器(例如粒子群优化器,模拟退火和差分演化)相比,我们还在12个优化基准问题的测试平台上评估了MSPS,表明它也可以在其他应用中进行探索。

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