Analyzing Sub-Classifications of Glaucoma via SOM Based Clustering of Optic Nerve Images.
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.
Yan
Sanjun
S
Health Informatics Lab, Faculty of Computer Science, Dalhousie University, Halifax, Canada.
Abidi
Syed Sibte Raza
SS
Artes
Paul Habib
PH
D000465:Algorithms; D016000:Cluster Analysis; D005901:Glaucoma; D006801:Humans; D007834:Lasers; D009898:Optic Disk; D009900:Optic Nerve; D014054:Tomography