Learning from healthy and stable eyes: A new approach for detection of glaucomatous progression

作者：

Highlights：

• We present a two-step framework for glaucoma progression detection.

• The Markov prior increases significantly the change detection rate.

• The one-class kernel classifier performs better than the fuzzy-based classifier.

• The use of the whole 3D SD-OCT images improves the glaucoma change detection.

摘要

Highlights•We present a two-step framework for glaucoma progression detection.•The Markov prior increases significantly the change detection rate.•The one-class kernel classifier performs better than the fuzzy-based classifier.•The use of the whole 3D SD-OCT images improves the glaucoma change detection.

论文关键词：Spatial dependency modeling,Copula theory,Kernel classifier,Glaucoma progression detection

论文评审过程：Received 4 August 2014, Revised 14 April 2015, Accepted 15 April 2015, Available online 23 April 2015, Version of Record 9 June 2015.

论文官网地址：https://doi.org/10.1016/j.artmed.2015.04.002