Name
Title | ||
|---|---|---|
Glaucoma Progression Detection Using Structural Retinal Nerve Fiber Layer Measurements and Functional Visual Field Points |
Abstract | ||
|---|---|---|
Machine learning classifiers were employed to detect glaucomatous progression using longitudinal series of structural data extracted from retinal nerve fiber layer thickness measurements and visual functional data recorded from standard automated perimetry tests. Using the collected data, a longitudinal feature vector was created for each patient's eye by computing the norm 1 difference vector of the data at the baseline and at each follow-up visit. The longitudinal features from each patient's eye were then fed to the machine learning classifier to classify each eye as stable or progressed over time. This study was performed using several machine learning classifiers including Bayesian, Lazy, Meta, and Tree, composing different families. Combinations of structural and functional features were selected and ranked to determine the relative effectiveness of each feature. Finally, the outcomes of the classifiers were assessed by several performance metrics and the effectiveness of structural and functional features were analyzed. |
Year | DOI | Venue |
|---|---|---|
2014 | 10.1109/TBME.2013.2295605 | Biomedical Engineering, IEEE Transactions |
Keywords | Field | DocType |
Bayes methods,biomedical optical imaging,eye,feature extraction,image classification,learning (artificial intelligence),medical image processing,neurophysiology,optical tomography,thickness measurement,trees (mathematics),vision defects,Bayesian classifier,Lazy classifier,Meta classifier,Tree classifier,functional visual field points,glaucoma progression detection,longitudinal feature vector,machine learning classifiers,norm 1 difference vector,standard automated perimetry tests,structural retinal nerve fiber layer thickness measurements,visual functional data,Biomedical engineering,biomedical signal processing,change detection,glaucoma progression,machine learning | Computer vision,Feature vector,Glaucoma,Pattern recognition,Naive Bayes classifier,Computer science,Optic disk,Feature extraction,Artificial intelligence,Meridian (perimetry, visual field),Visual field,Learning classifier system | Journal |
Volume | Issue | ISSN |
61 | 4 | 0018-9294 |
Citations | PageRank | References |
2 | 0.46 | 0 |
Authors | ||
10 | ||
Name | Order | Citations | PageRank |
|---|---|---|---|
| Siamak Yousefi | 1 | 86 | 13.41 |
| Michael H Goldbaum | 2 | 824 | 77.08 |
| Madhusudhanan Balasubramanian | 3 | 15 | 2.71 |
| Tzyy-Ping Jung | 4 | 1410 | 202.52 |
| Robert N Weinreb | 5 | 58 | 9.90 |
| Felipe A Medeiros | 6 | 9 | 2.65 |
| Linda M Zangwill | 7 | 14 | 3.19 |
| Jeffrey M Liebmann | 8 | 4 | 1.23 |
| Christopher A Girkin | 9 | 5 | 2.19 |
| Christopher Bowd | 10 | 22 | 4.53 |