基于非参数联合形状学习的定制形状建模。

Nonparametric joint shape learning for customized shape modeling.

机构信息

Sabanci University, Faculty of Engineering and Natural Sciences, Tuzla 34956, Istanbul, Turkey.

出版信息

Comput Med Imaging Graph. 2010 Jun;34(4):298-307. doi: 10.1016/j.compmedimag.2009.12.001. Epub 2009 Dec 30.

DOI:10.1016/j.compmedimag.2009.12.001

PMID:20044237

原文链接:https://pmc.ncbi.nlm.nih.gov/articles/PMC2972425/

Abstract

We present a shape optimization approach to compute patient-specific models in customized prototyping applications. We design a coupled shape prior to model the transformation between a related pair of surfaces, using a nonparametric joint probability density estimation. The coupled shape prior forces with the help of application-specific data forces and smoothness forces drive a surface deformation towards a desired output surface. We demonstrate the usefulness of the method for generating customized shape models in applications of hearing aid design and pre-operative to intra-operative anatomic surface estimation.

摘要

我们提出了一种形状优化方法，用于在定制化原型设计应用中计算患者特定的模型。我们设计了一个耦合形状先验，使用非参数联合概率密度估计来模拟一对相关表面之间的转换。耦合形状先验通过应用特定的数据力和光滑度力来驱动表面变形，使其朝着期望的输出表面进行。我们展示了该方法在助听器设计和术前到术中解剖表面估计等应用中生成定制形状模型的有效性。

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Proc IEEE Comput Soc Conf Comput Vis Pattern Recognit. 2004 Jun 27;1:I314-I319. doi: 10.1109/CVPR.2004.1315048.

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