Automatic feature point extraction for three-dimensional skull

FENG Jun; CHEN Yu; TONG Xin-long; HE Xiao-wei; ZHOU Ming-quan

doi:10.3788/OPE.20142205.1388

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Automatic feature point extraction for three-dimensional skull

更新时间：2020-08-13

- Automatic feature point extraction for three-dimensional skull
- Optics and Precision Engineering Vol. 22, Issue 5, Pages: 1388-1394(2014)
- 作者机构：
  
  1. 北京师范大学信息科学与技术学院北京,100875
  2. 西北大学信息科学与技术学院,陕西西安,710127
- 作者简介：
- 基金信息：
- DOI：10.3788/OPE.20142205.1388
  CLC： TP391.4
- Received：06 August 2013，
  
  Revised：29 September 2013，
  
  Published：25 May 2014
- 稿件说明：
移动端阅览
冯筠, 陈雨, 仝鑫龙等. 三维颅骨特征点的自动标定[J]. 光学精密工程, 2014,22(5): 1388-1394

FENG Jun, CHEN Yu, TONG Xin-long etc. Automatic feature point extraction for three-dimensional skull[J]. Editorial Office of Optics and Precision Engineering, 2014,22(5): 1388-1394
冯筠, 陈雨, 仝鑫龙等. 三维颅骨特征点的自动标定[J]. 光学精密工程, 2014,22(5): 1388-1394 DOI： 10.3788/OPE.20142205.1388.

FENG Jun, CHEN Yu, TONG Xin-long etc. Automatic feature point extraction for three-dimensional skull[J]. Editorial Office of Optics and Precision Engineering, 2014,22(5): 1388-1394 DOI： 10.3788/OPE.20142205.1388.

摘要

提出了颅骨特征点的全自动标定方法，该方法利用分区统计可变模型及模型相似性匹配的方法来标定颅骨特征点。首先，对颅骨分区样本进行统计建模；利用统计模型的形变控制生成基准模型和生成模型，并建立基准模型和生成模型间的映射关系。然后，定义了模型之间相似性。最后，利用模型相似度和映射关系，间接得到待测模型的特征点。实验结果表明：该方法定位眼眶模型特征点的位置平均误差值为3.2325 pixel；当距离阈值为10pixel（模型大小的3%）时，有90%的特征点的位置准确率达到100%。与现有方法相比，本文方法标定的颅骨特征点的准确度和精确度都更高，并且可以标定颅骨模型平滑区域的特征点。

Abstract

A fully automatic skull feature point extraction method was proposed

which extracts the skull feature points by a partitioned statistical deformable model and a model similarity matching method.First

the statistic models of skull partition were constructed

and a benchmark model and a series of generated models were built by statistical model deformation.Then the mapping relationship between models was established and the model similarity was defined.Finally

the feature points of the model to be measured were indirectly obtained with the model similarity and the projection relationship.Experimental results indicate that the location average error of the feature points for an eye socket model is about 3.232 5 pixels.When the distance threshold is 10 pixels (3% of the size of the model)

the location accuracy for 90% of the feature points achieves 100%.The method proposed has higher accuracy and exaction for skull feature point extraction as compared with traditional methods

and can extract the feature points of smooth regions for skull models.

关键词

Keywords

references

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Related Institution

School of Computer Science and Engineering， North Minzu University

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School of Information Engineering and Research Center of Digital Medical Image Technique, Zhengzhou University

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