Evidence map›Paper›PMID 31874641›Full record

ArticleBMC bioinformatics2019

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

Beiji Zou, Changlong Chen, Rongchang Zhao, Pingbo Ouyang, Chengzhang Zhu, Qilin Chen, Xuanchu Duan

Open access · goldAbstract read
PubMed Full text at PMC Publisher
In one paragraph

Article in BMC bioinformatics, 2019. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 1 paper.

0numbers the graph read from it
0cells of the map it votes in
1citing papers in PubMed
0.7field-weighted citation impact, top 29% of its field
1 · What the graph read from it

What it found

Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.

The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.

2 · The registry

The trial behind it

Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.

Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.

3 · Its place in the literature

Who cites it

1 citing paper in PubMed, 5 citations in OpenAlex.

  1. Analysis of international publication trends in artificial intelligence in ophthalmology.Graefe's archive for clinical and experimental ophthalmology = Albrecht von Graefes Archiv fur klinische und experimentelle Ophthalmologie · 2022
    Review
4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

7 authors at 2 institutions in 1 country.

Beiji ZouSchool of Computer Science and Engineering, Central South University, Changsha, 410083, China.
Changlong ChenSchool of Computer Science and Engineering, Central South University, Changsha, 410083, China.
Rongchang ZhaoSchool of Computer Science and Engineering, Central South University, Changsha, 410083, China. zhaorc100@163.com.
Pingbo OuyangSchool of Computer Science and Engineering, Central South University, Changsha, 410083, China.
Chengzhang ZhuSchool of Computer Science and Engineering, Central South University, Changsha, 410083, China.
Qilin ChenSchool of Computer Science and Engineering, Central South University, Changsha, 410083, China.
Xuanchu DuanThe Second Xiangya Hospital of Central South University, Changsha, 410011, China.
Central South University · CNSecond Xiangya Hospital of Central South University · CN

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

backgroundGlaucoma is an irreversible eye disease caused by the optic nerve injury. Therefore, it usually changes the structure of the optic nerve head (ONH). Clinically, ONH assessment based on fundus image is one of the most useful way for glaucoma detection. However, the effective representation for ONH assessment is a challenging task because its structural changes result in the complex and mixed visual patterns.

methodWe proposed a novel feature representation based on Radon and Wavelet transform to capture these visual patterns. Firstly, Radon transform (RT) is used to map the fundus image into Radon domain, in which the spatial radial variations of ONH are converted to a discrete signal for the description of image structural features. Secondly, the discrete wavelet transform (DWT) is utilized to capture differences and get quantitative representation. Finally, principal component analysis (PCA) and support vector machine (SVM) are used for dimensionality reduction and glaucoma detection.

resultsThe proposed method achieves the state-of-the-art detection performance on RIMONE-r2 dataset with the accuracy and area under the curve (AUC) at 0.861 and 0.906, respectively.

conclusionIn conclusion, we showed that the proposed method has the capacity as an effective tool for large-scale glaucoma screening, and it can provide a reference for the clinical diagnosis on glaucoma.

Indexed as

GlaucomaHumansOptic DiskRadonSupport Vector MachineWavelet AnalysisRadonComputer-aided diagnosisGlaucoma detectionRadon transform

Identifiers

PMID31874641
PMCPMC6929399
OpenAlexW2997767686

What OpenQuestion holds

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LicenceCC BY
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Registered trials

None linked

Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.