title: Segmentation of three-dimensional retinal image data.
abstract: We have combined methods from volume visualization and data analysis to support better diagnosis and treatment of human retinal diseases. Many diseases can be identified by abnormalities in the thicknesses of various retinal layers captured using optical coherence tomography (OCT). We used a support vector machine (SVM) to perform semi-automatic segmentation of retinal layers for subsequent analysis including a comparison of layer thicknesses to known healthy parameters. We have extended and generalized an older SVM approach to support better performance in a clinical setting through performance enhancements and graceful handling of inherent noise in OCT data by considering statistical characteristics at multiple levels of resolution. The addition of the multi-resolution hierarchy extends the SVM to have "global awareness." A feature, such as a retinal layer, can therefore be modeled.
authors.affiliation: Institute for Data Analysis and Visualization, University of California at Davis, USA. arfuller@ucdavis.edu
authors.forename: Alfred
authors.initials: A
authors.lastname: Fuller
authors.forename: Robert
authors.initials: R
authors.lastname: Zawadzki
authors.forename: Stacey
authors.initials: S
authors.lastname: Choi
authors.forename: David
authors.initials: D
authors.lastname: Wiley
authors.forename: John
authors.initials: J
authors.lastname: Werner
authors.forename: Bernd
authors.initials: B
authors.lastname: Hamann
mesh_terms: D000465:Algorithms; D001185:Artificial Intelligence; D006801:Humans; D007089:Image Enhancement / Q000379:methods*; D007090:Image Interpretation, Computer-Assisted / Q000379:methods*; D021621:Imaging, Three-Dimensional / Q000379:methods*; D010363:Pattern Recognition, Automated / Q000379:methods*; D012160:Retina / Q000033:anatomy & histology*; D042262:Retinoscopy / Q000379:methods*