June 2013
Volume 54, Issue 15
Free
ARVO Annual Meeting Abstract  |   June 2013
Automatic biometry of human anterior segment during accommodation
Author Affiliations & Notes
  • Meixiao Shen
    School of Ophthalmology & Optometry, Wenzhou Medical College, Wenzhou, China
  • Dexi Zhu
    School of Ophthalmology & Optometry, Wenzhou Medical College, Wenzhou, China
  • Yilei Shao
    School of Ophthalmology & Optometry, Wenzhou Medical College, Wenzhou, China
  • Lin Leng
    School of Ophthalmology & Optometry, Wenzhou Medical College, Wenzhou, China
  • Jianhua Wang
    Department of Ophthalmology, Bascom Palmer Eye Institute, University of Miami, Miami, FL
  • Fan Lu
    School of Ophthalmology & Optometry, Wenzhou Medical College, Wenzhou, China
  • Footnotes
    Commercial Relationships Meixiao Shen, None; Dexi Zhu, None; Yilei Shao, None; Lin Leng, None; Jianhua Wang, NIH (F), RPB (F); Fan Lu, None
  • Footnotes
    Support None
Investigative Ophthalmology & Visual Science June 2013, Vol.54, 3582. doi:
  • Views
  • Share
  • Tools
    • Alerts
      ×
      This feature is available to authenticated users only.
      Sign In or Create an Account ×
    • Get Citation

      Meixiao Shen, Dexi Zhu, Yilei Shao, Lin Leng, Jianhua Wang, Fan Lu, ; Automatic biometry of human anterior segment during accommodation. Invest. Ophthalmol. Vis. Sci. 2013;54(15):3582.

      Download citation file:


      © ARVO (1962-2015); The Authors (2016-present)

      ×
  • Supplements
Abstract

Purpose: To test accuracy, repeatability and robustness of a software algorithm that performs automatic biometry of human anterior segment of the eye imaged with long scan depth optical coherence tomography (OCT).

Methods: Imaging was performed with custom-built long scan depth OCT in 10 normal subjects. An automatic software algorithm including boundary segmentation, image registration and optical correction was developed for fast and reliable measurements of biometry during accommodation. The boundary segmentation algorithm mainly utilized the gradient information of image and applied the shortest path search based on the dynamic programming to optimize the edge finding. The automatic algorithm was validated with comparison of biometric dimensions between automated and manual measurements and repeatability study.

Results: The software algorithm successfully obtained biometric dimensions automatically including central corneal thickness (CCT), anterior chamber depth (ACD), pupil diameter ( PD), lens thickness (LT), radii of lens anterior curvature (LAC) and radii of lens posterior curvature (LPC) with batch processing. The execution time was about 5 seconds per 2D image (2048 x 2048 pixels) and less than 60 seconds per set of real time data during accommodation (32 images, 1024x4096 per image). For all biometric dimensions, there were no significant differences between the automatic and manual measurements. The intraclass correlation coefficients (ICC) of agreement between automatic and manual measurements ranged from 0.85 to 0.98 for all biometric dimensions. Similar with manual measurement, the coefficients of repeatability (CoRs) and ICC for all automated dimensions were good (1.1%- 6.1% and 0.663-0.990, respectively).

Conclusions: It demonstrates high accuracy, repeatability and fast execution speed for automated measurement of anterior segment dimensions. The application of this automatic software algorithm is promising for investigating dynamic changes of human anterior segment during accommodation in real-time.

Keywords: 549 image processing • 404 accommodation • 421 anterior segment  
×
×

This PDF is available to Subscribers Only

Sign in or purchase a subscription to access this content. ×

You must be signed into an individual account to use this feature.

×