›› 2017,Vol. 32 ›› Issue (6): 1214-1221.doi: 10.1007/s11390-017-1795-y

所属专题: Artificial Intelligence and Pattern Recognition

• Special Section on Selected Paper from NPC 2011 • 上一篇    下一篇

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. 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
  • 收稿日期:2017-06-20 修回日期:2017-09-26 出版日期:2017-11-05 发布日期:2017-11-05
  • 通讯作者: Hai-Lan Shen E-mail:hn shl@126.com
  • 作者简介: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.
  • 基金资助:

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

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. 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
  • Received:2017-06-20 Revised:2017-09-26 Online:2017-11-05 Published:2017-11-05
  • Contact: Hai-Lan Shen E-mail: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.
  • Supported by:

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

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%

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.

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