Literature DB >> 10484182

New software for lens retro-illumination digital image analysis.

A Gershenzon1, L D Robman.   

Abstract

PURPOSE: To describe functions of new software for cataract assessment and compare its validity with that of the Nidek EAS-1000 software (Nidek, Japan).
METHODS: A new Microsoft Windows 3.1/95 (Microsoft, USA) compliant software, Retroillumination Image Analysis (RIA), has been developed. The cataract reading is based on the contrasts in illumination between the opaque and transparent areas of the lens. Image loading, pupil detection and image analyses are automated. Differentiation between the different cataract types (cortical/posterior subscapsular) and other opacities is possible.
RESULTS: The program was tested on 233 Nidek EAS-1000 images. In all, 148 eyes had clear media or cortical cuneiform cataract, 37 also had confounding opacities and 48 had no confounding opacities but pupils were unevenly illuminated. The results of analysis with both Nidek EAS-1000 and RIA software were compared against clinical Wilmer grading. The correlation of cortical opacity size in Nidek EAS-1000 3.01c and RIA software, respectively, were 0.50* and 0.57* for whole data set, 0.54* and 0.55* for subgroup with no confounders or artefacts, 0.54* and 0.66* for subgroup with artefacts, 0.27 (P < 0.105) and 0.65* for subgroup with confounders (*P < 0.001).
CONCLUSION: The RIA software significantly improves the accuracy of cataract measurement in lens images with uneven pupil illumination or confounding opacities. Automation of (i) image loading, (ii) pupil detection and (iii) defining of the opacity area increases the efficiency of digital eye photography, eliminates human errors and speeds assessment.

Entities:  

Mesh:

Year:  1999        PMID: 10484182     DOI: 10.1046/j.1440-1606.1999.00201.x

Source DB:  PubMed          Journal:  Aust N Z J Ophthalmol        ISSN: 0814-9763


  4 in total

1.  Lens opacity detection for serious posterior subcapsular cataract.

Authors:  Wanjun Zhang; Huiqi Li
Journal:  Med Biol Eng Comput       Date:  2016-08-04       Impact factor: 2.602

2.  Deep Learning-Based Cataract Detection and Grading from Slit-Lamp and Retro-Illumination Photographs: Model Development and Validation Study.

Authors:  Ki Young Son; Jongwoo Ko; Eunseok Kim; Si Young Lee; Min-Ji Kim; Jisang Han; Eunhae Shin; Tae-Young Chung; Dong Hui Lim
Journal:  Ophthalmol Sci       Date:  2022-03-18

3.  Computer-aided assessment of diagnostic images for epidemiological research.

Authors:  Alison G Abraham; Donald D Duncan; Stephen J Gange; Sheila West
Journal:  BMC Med Res Methodol       Date:  2009-11-11       Impact factor: 4.615

Review 4.  A survey on computer aided diagnosis for ocular diseases.

Authors:  Zhuo Zhang; Ruchir Srivastava; Huiying Liu; Xiangyu Chen; Lixin Duan; Damon Wing Kee Wong; Chee Keong Kwoh; Tien Yin Wong; Jiang Liu
Journal:  BMC Med Inform Decis Mak       Date:  2014-08-31       Impact factor: 2.796

  4 in total

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