Unsupervised Gaussian Mixture-Model With Expectation Maximization for Detecting Glaucomatous Progression in Standard Automated Perimetry Visual Fields

PURPOSE: To validate Gaussian mixture-model with expectation maximization (GEM) and variational Bayesian independent component analysis mixture-models (VIM) for detecting glaucomatous progression along visual field (VF) defect patterns (GEM–progression of patterns (POP) and VIM-POP). To compare GEM-...

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Detalles Bibliográficos
Autores principales: Yousefi, Siamak, Balasubramanian, Madhusudhanan, Goldbaum, Michael H., Medeiros, Felipe A., Zangwill, Linda M., Weinreb, Robert N., Liebmann, Jeffrey M., Girkin, Christopher A., Bowd, Christopher
Formato: Online Artículo Texto
Lenguaje:English
Publicado: The Association for Research in Vision and Ophthalmology 2016
Materias:
Articles
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC4855479/
https://www.ncbi.nlm.nih.gov/pubmed/27152250
http://dx.doi.org/10.1167/tvst.5.3.2

Internet

https://www.ncbi.nlm.nih.gov/pmc/articles/PMC4855479/
https://www.ncbi.nlm.nih.gov/pubmed/27152250
http://dx.doi.org/10.1167/tvst.5.3.2

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