Improvement of feature point annotation for three-dimensional spine model based on curvature multiple feature fusion

Yu HUI; Jun-sheng WU; Bin YU; Chen ZHANG; Zhen WU

doi:10.3788/OPE.20162411.2872

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Improvement of feature point annotation for three-dimensional spine model based on curvature multiple feature fusion

Information science | 更新时间：2020-07-07

- Improvement of feature point annotation for three-dimensional spine model based on curvature multiple feature fusion
- Editorial Office of Optics and Precision Engineeri Vol. 24, Issue 11, Pages: 2872-2879(2016)
- 作者机构：
  
  1.西北工业大学计算机学院, 陕西西安 710072
  2.西北工业大学软件与微电子学院, 陕西西安 710072
  3.西安电子科技大学计算机学院, 陕西西安 710072
- 作者简介：
- 基金信息：
- DOI：10.3788/OPE.20162411.2872
  CLC： TP391.4
- Received：14 July 2016，
  
  Accepted：04 September 2016，
  
  Published：25 November 2016
- 稿件说明：
移动端阅览
Yu HUI, Jun-sheng WU, Bin YU, et al. Improvement of feature point annotation for three-dimensional spine model based on curvature multiple feature fusion[J]. Editorial office of optics and precision engineeri, 2016, 24(11): 2872-2879.
DOI：

Yu HUI, Jun-sheng WU, Bin YU, et al. Improvement of feature point annotation for three-dimensional spine model based on curvature multiple feature fusion[J]. Editorial office of optics and precision engineeri, 2016, 24(11): 2872-2879. DOI： 10.3788/OPE.20162411.2872.

摘要

由于脊椎生理结构的精准坐标描述和准确匹配尚未达到医学精度的要求，本文对如何精确描述脊柱腰骶段特征点的物理坐标进行研究。介绍了人体脊椎采样特征点的定义和传统标记方法。针对手动标注特征点精确度不够，易产生较大误差等问题提出了一种基于曲率多特征融合的自适应标注特征点的方法。该方法首先找出某个特征点的高斯曲率和平均曲率流的定义值，得到该特征点的法曲率相对极大值，并计算在指定极小半径

范围内的所有模型点的法曲率相对极大值。由于极大值曲率越大，三维模型表面在该点处的弯曲程度越大，该点就越能表现三维模型的大致轮廓，故以

范围内极大值最大的点作为特征点的曲率描述来替换手动拾取的点，从而准确反映该点的特征变化情况。最后，对改进结果进行偏差验证分析。结果表明：改进方法的准确度比现有手动标注特征点方法的准确度提高了约37%，验证了本文方法的有效性。

Abstract

Because the precise coordinate description and accurate matching of physiology of the lumber spine have not yet reached the accuracy requirement of the medicine

this paper researches how to describe the physical coordinates of lumber spine feature points precisely. The definition and traditional annotation method of human spine sample features were introduce. Then

an improved method to adaptively annotate the feature points was proposed based on curvature multiple feature fusing to solve the problem of weaker precision for the manual annotation of feature points. In this method

the definiting values of Gaussian curvature and average curvature of a certain feature point were found out firstly

and a relative maximum value of curvature of the feature point was obtained. Then

the relative maximum values of all the model points within a specified minimum radius

were obtained. Since the larger the curvature of maximum value was

the larger the crook degree of the 3-dimensioanl model surface on the point was

and the point can show the outline of the 3-dimensional model. Therefore

the maximum value was taken as the curvature description of the feature point to replace the manual pick up point

thus the feature variation of the point could be described precisely. Finally

the verification results were analyzed

and the results indicate that the improved method increases the accuracy by 37% as compared with that of traditional annotation methods. These results demonstrate that the new method is feasibility and effectiveness.

关键词

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