Robust multi-scale superpixel classification for optic cup localization

Ngan Meng Tan, Yanwu Xu, Wooi Boon Goh, Jiang Liu

Research output: Journal PublicationArticlepeer-review

44 Citations (Scopus)


This paper presents an optimal model integration framework to robustly localize the optic cup in fundus images for glaucoma detection. This work is based on the existing superpixel classification approach and makes two major contributions. First, it addresses the issues of classification performance variations due to repeated random selection of training samples, and offers a better localization solution. Second, multiple superpixel resolutions are integrated and unified for better cup boundary adherence. Compared to the state-of-the-art intra-image learning approach, we demonstrate improvements in optic cup localization accuracy with full cup-to-disc ratio range, while incurring only minor increase in computing cost.

Original languageEnglish
Pages (from-to)182-193
Number of pages12
JournalComputerized Medical Imaging and Graphics
Publication statusPublished - 1 Mar 2015
Externally publishedYes


  • Glaucoma
  • Model selection
  • Optic cup localization
  • Sparse learning
  • Superpixel classification

ASJC Scopus subject areas

  • Radiological and Ultrasound Technology
  • Radiology Nuclear Medicine and imaging
  • Computer Vision and Pattern Recognition
  • Health Informatics
  • Computer Graphics and Computer-Aided Design


Dive into the research topics of 'Robust multi-scale superpixel classification for optic cup localization'. Together they form a unique fingerprint.

Cite this