Tagging for improving feature points of human spine model by using Gaussian curvature and vector inner product

YU Bin; HUI Yu; WU Jun-sheng; ZHANG Chen; ZHANG Tian-tian

doi:10.3788/OPE.20172513.0250

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Tagging for improving feature points of human spine model by using Gaussian curvature and vector inner product

更新时间：2020-08-13

- Tagging for improving feature points of human spine model by using Gaussian curvature and vector inner product
- Optics and Precision Engineering Vol. 25, Issue 10s, Pages: 250-258(2017)
- 作者机构：
  
  1. 西安电子科技大学计算机学院西安,710072
  2. 西北工业大学计算机学院, 西安 710072
  3. 西北工业大学软件与微电子学院, 西安 710072
- 作者简介：
- 基金信息：
- DOI：10.3788/OPE.20172513.0250
  CLC： TP394.1
- Received：02 June 2017，
  
  Revised：20 June 2017，
  
  Published：25 November 2017
- 稿件说明：
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鱼滨, 惠宇, 武君胜等. 基于高斯曲率和向量内积的人体脊柱模型特征点的改进[J]. 光学精密工程, 2017,25(10s): 250-258

YU Bin, HUI Yu, WU Jun-sheng etc. Tagging for improving feature points of human spine model by using Gaussian curvature and vector inner product[J]. Editorial Office of Optics and Precision Engineering, 2017,25(10s): 250-258
鱼滨, 惠宇, 武君胜等. 基于高斯曲率和向量内积的人体脊柱模型特征点的改进[J]. 光学精密工程, 2017,25(10s): 250-258 DOI： 10.3788/OPE.20172513.0250.

YU Bin, HUI Yu, WU Jun-sheng etc. Tagging for improving feature points of human spine model by using Gaussian curvature and vector inner product[J]. Editorial Office of Optics and Precision Engineering, 2017,25(10s): 250-258 DOI： 10.3788/OPE.20172513.0250.

摘要

针对脊椎结构的复杂性和医学图像采样数据量庞大的问题，提出了一种利用高斯曲率和向量特征改进人体脊柱模型特征点的标注方法，该方法根据模型上某点局部邻域内各点的高斯曲率值和向量内积来动态调整手动标注点的物理坐标。首先，利用三维重建和网格划分生成人体脊椎三维网格模型；然后，手动拾取模型上一点作为初始拾取点，以极小半径R的球形空间作为拾取点的最大近似邻域，在此基础上求出点邻域内所有顶点的高斯曲率绝对值；最后，选取上述高斯曲率值中较大的

个点，分别求取这

个点与初始拾取点之间的向量内积，进而求出

个点中与初始拾取点间夹角角度最小的点，从而将初始拾取点坐标修正为上述夹角角度最小点的物理坐标。该方法在确保取得邻域内尽可能大的高斯曲率值的同时，可以使标记结果更趋近于真实的特征点，实验结果表明，该方法定位脊椎三维模型特征点的准确性约提高29%，且该方法针对脊椎模型的平滑区域具有较好的标注效果。

Abstract

A tagging method for improving feature points of human spine model by using Gauss curvature and the vector feature was proposed in the thesis to dynamically adjust physical coordinates of tagging points based on Gauss curvature values and vector inner products of various points located in local neighborhood in this model. Three-dimensional reconstruction and mesh dividing was used to generate 3D mesh model of thehuman spine; and then

a point in manual pick-up model was chosen as the initial pickup point. Spherical space with minimal radius R was regarded as the maximum approximate neighborhood of the pickup point

based on which absolute values of Gauss curvature for all vertexes in the neighborhood of the pickup point were calculated;

points with larger values from above Gauss curvature values were selected to calculate vector inner product between these n points and initial pickup point and further calculate the point from these points which had the minimum included angle with the initial pickup point

finally

coordinate of the initial pickup point would be corrected to the physical coordinate of the point which had the minimum included angle. The method makes sure that Gauss curvature value taken from the neighborhood is the largest possible value

it can make tagging results closer to the real feature pointat the same time. Experimental results show that the method improves the accuracy of positioning feature point of spinal 3D image by about 29%

besides

relatively good tagging results can be obtained by the method for smooth region of the spine model.

关键词

Keywords

references

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