Skip to main content

Sliding window and regression based cup detection in digital fundus images for glaucoma diagnosis.

Publication ,  Journal Article
Xu, Y; Xu, D; Lin, S; Liu, J; Cheng, J; Cheung, CY; Aung, T; Wong, TY
Published in: Med Image Comput Comput Assist Interv
2011

We propose a machine learning framework based on sliding windows for glaucoma diagnosis. In digital fundus photographs, our method automatically localizes the optic cup, which is the primary structural image cue for clinically identifying glaucoma. This localization uses a bundle of sliding windows of different sizes to obtain cup candidates in each disc image, then extracts from each sliding window a new histogram based feature that is learned using a group sparsity constraint. An epsilon-SVR (support vector regression) model based on non-linear radial basis function (RBF) kernels is used to rank each candidate, and final decisions are made with a non-maximal suppression (NMS) method. Tested on the large ORIGA(-light) clinical dataset, the proposed method achieves a 73.2% overlap ratio with manually-labeled ground-truth and a 0.091 absolute cup-to-disc ratio (CDR) error, a simple yet widely used diagnostic measure. The high accuracy of this framework on images from low-cost and widespread digital fundus cameras indicates much promise for developing practical automated/assisted glaucoma diagnosis systems.

Duke Scholars

Published In

Med Image Comput Comput Assist Interv

DOI

Publication Date

2011

Volume

14

Issue

Pt 3

Start / End Page

1 / 8

Location

Germany

Related Subject Headings

  • Regression Analysis
  • Photography
  • Pattern Recognition, Automated
  • Optic Disk
  • Models, Statistical
  • Image Processing, Computer-Assisted
  • Humans
  • Glaucoma
  • Fundus Oculi
  • Diagnostic Techniques, Ophthalmological
 

Citation

APA
Chicago
ICMJE
MLA
NLM
Xu, Y., Xu, D., Lin, S., Liu, J., Cheng, J., Cheung, C. Y., … Wong, T. Y. (2011). Sliding window and regression based cup detection in digital fundus images for glaucoma diagnosis. Med Image Comput Comput Assist Interv, 14(Pt 3), 1–8. https://doi.org/10.1007/978-3-642-23626-6_1
Xu, Yanwu, Dong Xu, Stephen Lin, Jiang Liu, Jun Cheng, Carol Y. Cheung, Tin Aung, and Tien Yin Wong. “Sliding window and regression based cup detection in digital fundus images for glaucoma diagnosis.Med Image Comput Comput Assist Interv 14, no. Pt 3 (2011): 1–8. https://doi.org/10.1007/978-3-642-23626-6_1.
Xu Y, Xu D, Lin S, Liu J, Cheng J, Cheung CY, et al. Sliding window and regression based cup detection in digital fundus images for glaucoma diagnosis. Med Image Comput Comput Assist Interv. 2011;14(Pt 3):1–8.
Xu, Yanwu, et al. “Sliding window and regression based cup detection in digital fundus images for glaucoma diagnosis.Med Image Comput Comput Assist Interv, vol. 14, no. Pt 3, 2011, pp. 1–8. Pubmed, doi:10.1007/978-3-642-23626-6_1.
Xu Y, Xu D, Lin S, Liu J, Cheng J, Cheung CY, Aung T, Wong TY. Sliding window and regression based cup detection in digital fundus images for glaucoma diagnosis. Med Image Comput Comput Assist Interv. 2011;14(Pt 3):1–8.

Published In

Med Image Comput Comput Assist Interv

DOI

Publication Date

2011

Volume

14

Issue

Pt 3

Start / End Page

1 / 8

Location

Germany

Related Subject Headings

  • Regression Analysis
  • Photography
  • Pattern Recognition, Automated
  • Optic Disk
  • Models, Statistical
  • Image Processing, Computer-Assisted
  • Humans
  • Glaucoma
  • Fundus Oculi
  • Diagnostic Techniques, Ophthalmological