非刚性曲面匹配技术在脊柱侧凸变形建模中的应用

K. Ang
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引用次数: 1

摘要

我们提出了一种新的变形建模方法,并将其应用于脊柱侧凸监测。尽管广泛使用表面形貌来监测脊柱侧凸,但由于脊柱侧凸畸形引起的形状变化从未得到满意的解决。提出了一种包含九个新参数的非刚性曲面匹配算法。这种非刚性匹配算法已经在具有可预测地形变形的模型中进行了试验。有证据表明,表面变形是可以建模的。此外,为了证明这种新的非刚性匹配算法在脊柱侧凸建模中的能力,我们使用四种不同的脊柱侧凸数据集与经典的刚性匹配算法进行了实验比较。非刚性匹配算法返回的均方根值提高了至少10%。实验结果表明,这种新的非刚性匹配算法能够提高匹配的精度和准确度。分析表明,这种新的非刚性匹配算法是一种非常成功的工具,是对经典方法的改进。同时,新参数能够描绘地表变形可能的空间分布。1. 背景
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Non-Rigid Surface Matching Technique as a Tool in Scoliosis Deformation Modelling
We present a novel approach for deformation modelling and exploit it on scoliosis monitoring. Despite the extensive use of surface topographies to monitor scoliosis, shape change due to scoliosis deformities has never been satisfactorily resolved. A novel non-rigid surface matching algorithm with nine new parameters has been investigated and developed. This non-rigid matching algorithm has been trialled using models with predictable topographic deformation. There is evidence that surface deformities can be modelled. In addition, to demonstrate the capability of this new non-rigid matching algorithm in scoliosis modelling, we have performed experimental comparison with classical rigid matching algorithm using four different scoliosis data sets. The non-rigid matching algorithm returned r.m.s. values which were improved by at least 10%. The experimental results are very promising, demonstrating that this new non-rigid matching algorithm is able to improve the precision and the accuracy of the matching. Analysis indicates that this new non-rigid matching algorithm has proven to be a very successful tool and is an improvement on the classical approach. Meanwhile the new parameters are able to delineate the possible spatial distribution of surface deformation. 1. Background
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