title: Analyzing Sub-Classifications of Glaucoma via SOM Based Clustering of Optic Nerve Images.
abstract: We present a data mining framework to cluster optic nerve images obtained by Confocal Scanning Laser Tomography (CSLT) in normal subjects and patients with glaucoma. We use self-organizing maps and expectation maximization methods to partition the data into clusters that provide insights into potential sub-classification of glaucoma based on morphological features. We conclude that our approach provides a first step towards a better understanding of morphological features in optic nerve images obtained from glaucoma patients and healthy controls.
authors.affiliation: Health Informatics Lab, Faculty of Computer Science, Dalhousie University, Halifax, Canada.
authors.forename: Sanjun
authors.initials: S
authors.lastname: Yan
authors.forename: Syed Sibte Raza
authors.initials: SS
authors.lastname: Abidi
authors.forename: Paul Habib
authors.initials: PH
authors.lastname: Artes
mesh_terms: D000465:Algorithms; D016000:Cluster Analysis; D005901:Glaucoma; D006801:Humans; D007834:Lasers; D009898:Optic Disk; D009900:Optic Nerve; D014054:Tomography