? 基于主动轮廓和能量约束的筛板前表面深度自动测量
Journal of Computer Science and Technology
Quick Search in JCST
 Advanced Search 
      Home | PrePrint | SiteMap | Contact Us | Help
 
Indexed by   SCIE, EI ...
Bimonthly    Since 1986
Journal of Computer Science and Technology 2017, Vol. 32 Issue (6) :1214-1221    DOI: 10.1007/s11390-017-1795-y
Special Section of CAD/Graphics 2017 << Previous Articles | Next Articles >>
基于主动轮廓和能量约束的筛板前表面深度自动测量
Zai-Liang Chen1,2,3, Member, CCF, Peng Peng1,2, Bei-Ji Zou1,2,3, Member, CCF, Hai-Lan Shen1,3,*, Member, CCF, Hao Wei1,2, Rong-Chang Zhao1,3
1 School of Information Science and Engineering, Central South University, Changsha 410083, China;
2 Center for Ophthalmic Imaging Research, Central South University, Changsha 410083, China;
3 "Mobile Health" Ministry of Education-China Mobile Joint Laboratory, Central South University Changsha 410083, China
Automatic Anterior Lamina Cribrosa Surface Depth Measurement Based on Active Contour and Energy Constraint
Zai-Liang Chen1,2,3, Member, CCF, Peng Peng1,2, Bei-Ji Zou1,2,3, Member, CCF, Hai-Lan Shen1,3,*, Member, CCF, Hao Wei1,2, Rong-Chang Zhao1,3
1 School of Information Science and Engineering, Central South University, Changsha 410083, China;
2 Center for Ophthalmic Imaging Research, Central South University, Changsha 410083, China;
3 "Mobile Health" Ministry of Education-China Mobile Joint Laboratory, Central South University Changsha 410083, China

摘要
参考文献
相关文章
Download: [PDF 3506KB]  
摘要 视神经筛板被证明与青光眼最大的风险因素——眼内压存在一定关联。然而由于图像质量以及筛板由于青光眼引起的向后移位等原因,导致我们对活体筛板的成像与评估能力受到一定的限制。本文提出了一种筛板前表面深度的自动测量方法,包括一种基于K-means和主动轮廓算法的Bruch膜开口点(BMO)测定方法和一种基于能量约束的筛板前表面分割方法。在BMO测定中,我们使用K-means的结果用以对主动轮廓模型进行初始化;而在筛板前表面分割中,我们使用能量函数在图像每一个A-scan上寻找到一个候选点,再通过约束将不满足条件的候选点剔除,最后使用b样条拟合方法得到结果。实验结果表明我们提出的方法在BMO测定中达到45.34微米的平均误差,与手动标定极为接近;而在筛板前表面分割中,我们分割出的曲线上平均误差低于5个像素的点的比例达到94.1%,低于3个像素的达到76.1%
关键词筛板前表面分割   主动轮廓   能量约束   测量     
Abstract: The lamina cribrosa is affected by intraocular pressure, which is the major risk of glaucoma. However, the capability to evaluate the lamina cribrosa in vivo has been limited until recently due to poor image quality and the posterior laminar displacement of glaucomatous eyes. In this study, we propose an automatic method to measure the anterior lamina cribrosa surface depth (ALCSD), including a method for detecting Bruch's membrane opening (BMO) based on k-means and region-based active contour. An anterior lamina cribrosa surface segmentation method based on energy constraint is also proposed. In BMO detection, we initialize the Chan-Vese active contour model by using the segmentation map of the k-means cluster. In the segmentation of anterior lamina cribrosa surface, we utilize the energy function in each A-scan to establish a set of candidates. The points in the set that fail to meet the constraints are removed. Finally, we use the B-spline fitting method to obtain the results. The proposed automatic method can model the posterior laminar displacement by measuring the ALCSD. This method achieves a mean error of 45.34 μm in BMO detection. The mean errors of the anterior lamina cribrosa surface are 94.1% within five pixels and 76.1% within three pixels.
Keywordsanterior lamina cribrosa surface segmentation   active contour   energy constraint   measurement     
Received 2017-06-20;
本文基金:

This work was supported by the National Natural Science Foundation of China under Grant Nos. 61672542 and 61573380.

通讯作者: Hai-Lan Shen     Email: hn shl@126.com
About author: Zai-Liang Chen received his Ph.D.degree in computer science from Central South University,Changsha,in 2012.He is currently an associate professor with Central South University,Changsha,and the associate director of the Center for Ophthalmic Imaging Research,Central South University,Changsha.
引用本文:   
Zai-Liang Chen, Peng Peng, Bei-Ji Zou, Hai-Lan Shen, Hao Wei, Rong-Chang Zhao.基于主动轮廓和能量约束的筛板前表面深度自动测量[J]  Journal of Computer Science and Technology , 2017,V32(6): 1214-1221
Zai-Liang Chen, Peng Peng, Bei-Ji Zou, Hai-Lan Shen, Hao Wei, Rong-Chang Zhao.Automatic Anterior Lamina Cribrosa Surface Depth Measurement Based on Active Contour and Energy Constraint[J]  Journal of Computer Science and Technology, 2017,V32(6): 1214-1221
链接本文:  
http://jcst.ict.ac.cn:8080/jcst/CN/10.1007/s11390-017-1795-y
Copyright 2010 by Journal of Computer Science and Technology