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首页> 外文期刊>Studies in Health Technology and Informatics >Non-rigid Surface Shape Registration to Monitor Change in Back Surface Topography
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Non-rigid Surface Shape Registration to Monitor Change in Back Surface Topography

机译:非刚性表面形状配准以监视背面形貌的变化

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Measured back surface topographies can be useful to monitor the external manifestation of scoliosis to avoid exposure to large doses of radiation. Manual shape fitting of back surface topographies from successive clinical visits can then be used to detect differences. Automated matching of the measured topographies has been seen as a possible improvement on manual comparisons. Recognizing that two changed surface cannot be expected to be simple rigid replicas of each other, the goal of this research has been to develop a new algorithm based on a non-rigid surface matching algorithm, suited to matching the surfaces into same reference frame while also estimating the parameters of the scoliosis deformities, and eliminating noise due to normal body change caused by growth. Back surface topography data from laser optical scanning have been automatically matched by a least squares non-rigid matching algorithm. The algorithm includes new parameters able to model shape changes caused by normal growth and scoliosis deformation. This non-rigid matching algorithm returned r.m.s. values for surface closeness which were improved by at least 10% over rigid matching. Experiments on various scoliosis data demonstrate that the non-rigid matching algorithm is able to accurately match the surfaces while simultaneously extracting parameters representing patient shape change. The non-rigid algorithm has proven to be an improvement on the classical rigid surface matching approach which allows positional fit rather than shape fit. Measured back surface topographies can be closely matched to monitor the external manifestation of scoliosis.
机译:测得的背面形貌可用于监测脊柱侧凸的外部表现,以避免暴露于大剂量的辐射下。然后可以使用来自连续临床就诊的背面形貌的手动形状拟合来检测差异。可以将测量地形的自动匹配视为手动比较的一种可能的改进。认识到不能期望两个变化的表面是彼此的简单刚性副本,因此本研究的目标是开发一种基于非刚性表面匹配算法的新算法,该算法适用于将表面匹配到相同的参考系中,同时估计脊柱侧弯畸形的参数,并消除由于生长引起的正常身体变化而产生的噪音。来自激光光学扫描的背面形貌数据已通过最小二乘非刚性匹配算法自动匹配。该算法包括能够模拟由正常生长和脊柱侧弯变形引起的形状变化的新参数。此非刚性匹配算法返回r.m.s。表面紧密度的值比刚性匹配提高了至少10%。在各种脊柱侧弯数据上的实验表明,非刚性匹配算法能够精确匹配表面,同时提取代表患者形状变化的参数。事实证明,非刚性算法是对经典刚性表面匹配方法的一种改进,该方法允许位置拟合而不是形状拟合。测得的背面形貌可以紧密匹配,以监测脊柱侧凸的外部表现。

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