title: Mean curvature mapping for detection of corneal shape abnormality.
abstract: Corneal topography is used to measure the anterior surface of the cornea. It is conventionally represented as radial slope, radial curvature, and elevation. In this paper, we introduce the application of mean curvature mapping as an alternative representation of the corneal topography. The purpose is to improve the detection of keratoconus and other diseases characterized by local increase in corneal curvature. Both simulated keratoconic cornea and real keratoconus data exported from the corneal topography system were analyzed. Four representations of corneal topography were generated and compared. It was found that mean curvature mapping provided the most precise cone location in simulated keratoconus. In both actual and simulated keratoconus cases, the appearance of the cone-like distortion is more consistent on mean curvature maps. Mean curvature mapping may improve the detection and localization of corneal shape abnormalities.
authors.affiliation: maolongt@usc.edu
authors.forename: Maolong
authors.initials: M
authors.lastname: Tang
authors.forename: Raj
authors.initials: R
authors.lastname: Shekhar
authors.forename: David
authors.initials: D
authors.lastname: Huang
mesh_terms: D000465:Algorithms; D001185:Artificial Intelligence; D016000:Cluster Analysis; D003315:Cornea / Q000473:pathology*; D019781:Corneal Topography / Q000379:methods*; D006801:Humans; D007089:Image Enhancement / Q000379:methods; D007090:Image Interpretation, Computer-Assisted / Q000379:methods*; D021621:Imaging, Three-Dimensional / Q000379:methods*; D016247:Information Storage and Retrieval / Q000379:methods; D007640:Keratoconus / Q000145:classification / Q000473:pathology*; D010363:Pattern Recognition, Automated / Q000379:methods*; D015203:Reproducibility of Results; D012680:Sensitivity and Specificity; D013382:Subtraction Technique