Robust multi-scale superpixel classification for optic cup localization. (March 2015)

• Record Type: Journal Article
• Authors: Tan, Ngan-Meng; Xu, Yanwu; Goh, Wooi Boon; Liu, Jiang
• Is Part Of: Computerized medical imaging and graphics. Volume 40(2015)
• Page Start: 182
• Page End: 193
• Publication Date: 2015-03
• Subjects: Optic cup localization -- Glaucoma -- Model selection -- Superpixel classification -- Sparse learning; Diagnostic imaging -- Periodicals; Imaging systems in medicine -- Periodicals; Diagnosis, Radioscopic -- Data processing -- Periodicals; Diagnostic Imaging -- Periodicals; Imagerie pour le diagnostic -- Périodiques; Diagnostic imaging; Periodicals; Electronic journals; Electronic journals; 616.0754
• DOI: 10.1016/j.compmedimag.2014.10.002
• Languages: English
• ISSNs: 0895-6111

Abstract

Abstract : Graphical abstract: Abstract : Highlights: Glaucoma is a leading cause of vision loss, & optic cup detection is of great interest. An optimal model integration framework to robustly localize the optic cup is presented. It addresses performance variations from random repeated training. Multiple superpixel scales are also integrated for better cup boundary adherence. It outperforms the intra image learning approach in cup localization accuracy. Abstract: 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.