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Robust medical image elastic registration using global optimisation strategy in frequency domain

机译:使用频域全局优化策略的稳健医学图像弹性配准

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摘要

A new global optimisation strategy in frequency domain (GOFD) is presented and applied in medical image elastic registration. The method is consists of a global optimisation phase for rough searching and a local optimisation phase for fine searching. Rough searching is based on the random sampling technique in the frequency domain. According to the sampling theory, when the sampling frequency is higher than twice the maximum frequency of a function, the function can be completely reconstructed from these finite sampling points. The maximum (or minimum) value of the function at these finite sampling points is approximately in the global extreme. To obtain the exact global extreme, fine searching is performed in the small neighbourhood of the point corresponding to the approximate global maximum value. The new method presented can theoretically ensure that the global optimisation solution is found. The experiments show that our new method is more robust and accurate than other elastic registration algorithms.
机译:提出了一种新的频域全局优化策略,并将其应用于医学图像弹性配准中。该方法包括用于粗略搜索的全局优化阶段和用于精细搜索的局部优化阶段。粗搜索基于频域中的随机采样技术。根据采样理论,当采样频率高于某个函数最大频率的两倍时,可以从这些有限采样点中完全重建该函数。在这些有限采样点处函数的最大值(或最小值)大约在全局极限范围内。为了获得精确的全局极值,在与近似全局最大值相对应的点的小邻域中执行精细搜索。提出的新方法从理论上可以确保找到全局优化解。实验表明,我们的新方法比其他弹性配准算法更健壮和准确。

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