Bao FangJun, Wang JunJie, Huang JinHai, Yu Ye, Deng ManLi, Li LinNa, Yu Ayong, Wang QinMei, Davey Pinakin Gunvant, Elsheikh Ahmed
The Affiliated Eye Hospital of WenZhou Medical University, Wenzhou, 325027, China.
The institution of ocular biomechanics, Wenzhou Medical University, Wenzhou, Zhejiang Province 325027, China.
PLoS One. 2015 Nov 23;10(11):e0139541. doi: 10.1371/journal.pone.0139541. eCollection 2015.
To improve the reliability of corneal topographic data through the development of a method to estimate the magnitude of misalignment between successive corneal videokeratography (VK) maps and eliminate the effect of misalignment on the repeatability of topography data.
Anterior and posterior topography maps were recorded twice for 124 healthy eyes of 124 participants using a Pentacam, and the repeatability of measurements was assessed by calculating the differences in elevation between each two sets of data. The repeatability of measurements was re-assessed following the determination of the magnitude of misalignment components (translational displacements: x0, y0 and z0, and rotational displacements: α, β and γ) between each two data sets and using them to modify the second data set within each pair based on an Iterative Closest Point (ICP) algorithm. The method simultaneously considered the anterior and posterior maps taken for the same eye since they were assumed to have the same set of misalignment components. A new parameter, named Combined Misalignment parameter (CM), has been developed to combine the effect of all six misalignment components on topography data and so enable study of the association between misalignment and the data repeatability test results.
The repeatability tests resulted in average root mean square (RMS) differences in elevation data of 8.46±2.75 μm before ICP map matching when simultaneously considering anterior and posterior surfaces. With map matching and misalignment correction, the differences decreased to 7.28±2.58 μm (P = 0.00). When applied to only the anterior maps, misalignment correction led to a more pronounced reduction in elevation data differences from 4.58±1.84 μm to 2.97±1.29 μm (P = 0.00). CM was found to be associated with the repeatability error (P = 0.00), with posterior maps being responsible for most of the error due to their relatively lower accuracy compared to anterior maps.
The ICP algorithm can be used to estimate, and effectively correct for, the potential misalignment between successive corneal videokeratography maps.
通过开发一种方法来估计连续角膜视频角膜地形图（VK）图之间的错位大小，并消除错位对地形图数据可重复性的影响，从而提高角膜地形图数据的可靠性。
使用Pentacam对124名参与者的124只健康眼睛的前后地形图进行两次记录，并通过计算每组两组数据之间的高度差异来评估测量的可重复性。在确定每组两组数据集之间的错位分量（平移位移：x0、y0和z0，以及旋转位移：α、β和γ）的大小并使用迭代最近点（ICP）算法基于它们修改每对中的第二个数据集之后，重新评估测量的可重复性。该方法同时考虑了同一眼睛拍摄的前后图，因为假设它们具有相同的一组错位分量。已经开发了一个名为组合错位参数（CM）的新参数，以结合所有六个错位分量对地形图数据的影响，从而能够研究错位与数据可重复性测试结果之间的关联。
在同时考虑前后表面时，重复性测试在ICP图匹配之前导致高度数据的平均均方根（RMS）差异为8.46±2.75μm。通过图匹配和错位校正，差异降至7.28±2.58μm（P = 0.00）。当仅应用于前图时，错位校正导致高度数据差异从4.58±1.84μm更显著地降低至2.97±1.29μm（P = 0.00）。发现CM与可重复性误差相关（P = 0.00），由于后图与前图相比精度相对较低，后图是大部分误差的原因。
ICP算法可用于估计并有效校正连续角膜视频角膜地形图之间的潜在错位。
