中国组织工程研究

中国组织工程研究 ›› 2010, Vol. 14 ›› Issue (9): 1525-1530.doi: 10.3969/j.issn.1673-8225.2010.09.002

• 数字化骨科 • 上一篇    下一篇

一种改进形状插值方法在恒河猴脑黑质断层图像中的应用

朱柳红，罗述谦   

  1. 首都医科大学生物医学工程学院，　北京市　   100069
  • 出版日期:2010-02-26 发布日期:2010-02-26
  • 通讯作者: 罗述谦，教授，博士生导师，首都医科大学生物医学工程学院，北京市　 100069 sqluo@ieee.org
  • 作者简介:朱柳红，女，1985年生，福建省龙岩市人，汉族，首都医科大学生物医学工程学院在读硕士，主要从事医学图像处理方面的研究。 zhuliuhong@gmail.com

Application of improved shape-based interpolation algorithm in substantia nigra slices of rhesus monkeys

Zhu Liu-hong, Luo Shu-qian   

  1. College of Biomedical Engineering, Capital Medical University, Beijing   100069, China
  • Online:2010-02-26 Published:2010-02-26
  • Contact: Professor, Doctoral supervisor, College of Biomedical Engineering, Capital Medical University, Beijing 100069, China sqluo@ieee.org
  • About author:Zhu Liu-hong, Studying for master’s degree, College of Biomedical Engineering, Capital Medical University, Beijing 100069, China zhuliuhong@gmail.com

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442

被引次数

1

摘要：

背景：研究表明，脑黑质结构的病变是导致帕金森病的主要原因。从影像学角度来看，结构微小的黑质空间位置信息、体积以及3D结构形态的分析为帕金森病的临床诊断和治疗效果评价提供了十分有力的工具，因此其三维形态的研究工作尤为重要。由于恒河猴与人类的生理十分相似，这使它成为许多科学研究中比较理想的实验对象。
目的：对传统的形状插值方法进行改进，并运用于恒河猴脑黑质断层图像。
方法：较常用的形状插值方法是对变换后的相邻两层距离图像进行线性加权平均，从而获取中间层。试验中考虑多层相邻图像空间位置对插值图像的影响，然后进行非线性加权获取中间层。
结果与结论：对插值的方法进行评估，评估结果比较令人满意，并运用于恒河猴脑黑质的三维重建中。该形状插值改进方法可以较好地运用于层片稀疏且结构细微的核团插值工作中，从而为与细微结构相关的疾病研究提供一定的参考价值。

关键词: 恒河猴, 脑黑质, 形状插值, 非线性加权, 距离变换, 三维重建, 数字化医学

Abstract:

BACKGROUND: Studies have shown that pathological change of substantia nigra (SN) is the main reason for Parkinson's disease (PD). From the medical imaging, spatial location, size and 3D morphology analysis of the tiny SN structure are very important to PD’s diagnosis and treatment evaluation. As the physiology of rhesus monkey is much similar to human’s, it has been used as an ideal experiment subject in many scientific researches.
OBJECTIVE: To modify traditional shape-based interpolation method for SN structure of the rhesus monkey brain slices.
METHODS: To gain the middle layers between two slices, traditional shape-based interpolation used linear weighted mean algorithm. However, considering the impact of adjacent multi-layer to the image, the middle layers were obtained by non-linear weight.
RESULTS AND CONCLUSION: The new interpolation algorithm was assessed with a favorable results. Also, this method was applied to the 3D reconstruction of SN structure. The results show that the new interpolation method can be used in nucleus group interpolation with sparse slices and tiny structures, and offer reference to the related disease research.

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