title: Geometric corner extraction in retinal fundus images.
abstract: This paper presents a novel approach of finding corner features between retinal fundus images. Such images are relatively textureless and comprising uneven shades which render state-of-the-art approaches e.g., SIFT to be ineffective. Many of the detected features have low repeatability (<; 10%), especially when the viewing angle difference in the corresponding images is large. Our approach is based on the finding of blood vessels using a robust line fitting algorithm, and locating corner features based on the bends and intersections between the blood vessels. These corner features have proven to be superior to the state-of-the-art feature extraction methods (i.e. SIFT, SURF, Harris, Good Features To Track (GFTT) and FAST) with regard to repeatability and stability in our experiment. Overall in average, the approach has close to 10% more repeatable detected features than the second best in two corresponding retinal images in the experiment.
authors.forename: Jimmy Addison
authors.initials: JA
authors.lastname: Lee
authors.forename: Beng Hai
authors.initials: BH
authors.lastname: Lee
authors.forename: Guozhen
authors.initials: G
authors.lastname: Xu
authors.forename: Ee Ping
authors.initials: EP
authors.lastname: Ong
authors.forename: Damon Wing Kee
authors.initials: DW
authors.lastname: Wong
authors.forename: Jiang
authors.initials: J
authors.lastname: Liu
authors.forename: Tock Han
authors.initials: TH
authors.lastname: Lim
mesh_terms: D000465:Algorithms; D005654:Fundus Oculi; D006801:Humans; D007090:Image Interpretation, Computer-Assisted; D012171:Retinal Vessels / Q000473:pathology