Literature DB >> 16043858

Analysis of GDx-VCC polarimetry data by Wavelet-Fourier analysis across glaucoma stages.

Edward A Essock1, Yufeng Zheng, Pinakin Gunvant.   

Abstract

PURPOSE: The purpose of this study was to apply shape-based analysis techniques of retinal nerve fiber layer (RNFL) thickness to GDx-VCC (variable corneal and lens compensator; Laser Diagnostic Technologies, Inc., San Diego, CA) polarimetry data and to evaluate the techniques' ability to detect glaucoma in its earliest stages. Wavelet-based (wavelet-Fourier analysis [WFA]), Fourier-based (fast Fourier analysis [FFA]), and several previous variations of shape-based analysis were considered, as well as the standard metric nerve fiber indicator (NFI), and all were compared as a function of disease stage.
METHODS: GDx-VCC scans of one eye of each of 67 patients with glaucoma and each of 67 healthy age-matched subjects provided RNFL thickness estimates at a fixed distance from the optic disc. Severity of disease was graded according to the Glaucoma Staging System and also by mean deviation (MD) from standard automated perimetry. WFA, FFA, and NFI procedures were performed including the following variations: use of signed or unsigned phase, inclusion of interocular or intraocular asymmetry of analysis parameters, and combination of features by principle components analysis or Wilks lambda. Independent samples (k-fold variation) were used for training and testing. Sensitivity, specificity, and receiver operating characteristic (ROC) area were obtained.
RESULTS: Classification performance of WFA (ROC = 0.978) was significantly better than FFA (ROC = 0.938) and NFI (ROC = 0.900). This difference was largest for the earliest stages of glaucoma. Shape-based analysis methods performed better than NFI overall. Adding between-eye asymmetry measures helped FFA but not WFA.
CONCLUSIONS: Shape-based analysis, and WFA in particular, makes an important improvement in detecting earliest glaucoma with polarimetry.

Entities:  

Mesh:

Year:  2005        PMID: 16043858     DOI: 10.1167/iovs.04-1156

Source DB:  PubMed          Journal:  Invest Ophthalmol Vis Sci        ISSN: 0146-0404            Impact factor:   4.799


  7 in total

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Authors:  Michael D Twa; Srinivasan Parthasarathy; Chris A Johnson; Mark A Bullimore
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2.  Linear discriminant analysis and artificial neural network for glaucoma diagnosis using scanning laser polarimetry-variable cornea compensation measurements in Taiwan Chinese population.

Authors:  Mei-Ling Huang; Hsin-Yi Chen; Wei-Cheng Huang; Yi-Yu Tsai
Journal:  Graefes Arch Clin Exp Ophthalmol       Date:  2009-12-15       Impact factor: 3.117

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Journal:  Cochrane Database Syst Rev       Date:  2015-11-30

Review 4.  Diagnostic tools for glaucoma detection and management.

Authors:  Pooja Sharma; Pamela A Sample; Linda M Zangwill; Joel S Schuman
Journal:  Surv Ophthalmol       Date:  2008-11       Impact factor: 6.048

5.  Optic nerve head and retinal nerve fiber layer analysis: a report by the American Academy of Ophthalmology.

Authors:  Shan C Lin; Kuldev Singh; Henry D Jampel; Elizabeth A Hodapp; Scott D Smith; Brian A Francis; David K Dueker; Robert D Fechtner; John S Samples; Joel S Schuman; Don S Minckler
Journal:  Ophthalmology       Date:  2007-10       Impact factor: 12.079

6.  Localization and diagnosis framework for pediatric cataracts based on slit-lamp images using deep features of a convolutional neural network.

Authors:  Xiyang Liu; Jiewei Jiang; Kai Zhang; Erping Long; Jiangtao Cui; Mingmin Zhu; Yingying An; Jia Zhang; Zhenzhen Liu; Zhuoling Lin; Xiaoyan Li; Jingjing Chen; Qianzhong Cao; Jing Li; Xiaohang Wu; Dongni Wang; Haotian Lin
Journal:  PLoS One       Date:  2017-03-17       Impact factor: 3.240

7.  A novel glaucomatous representation method based on Radon and wavelet transform.

Authors:  Beiji Zou; Changlong Chen; Rongchang Zhao; Pingbo Ouyang; Chengzhang Zhu; Qilin Chen; Xuanchu Duan
Journal:  BMC Bioinformatics       Date:  2019-12-24       Impact factor: 3.169

  7 in total

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