Evidence map›Paper›PMID 29745528›Full record

ArticleSheng wu yi xue gong cheng xue za zhi = Journal of biomedical engineering = Shengwu yixue gongchengxue zazhi2018

[A novel method of optic disk segmentation based on visual saliency and rotary scanning].

Xinrong Cao, Lanyan Xue, Jiawen Lin, Lun Yu

Abstract readEnglish Abstract
In one paragraph

Article in Sheng wu yi xue gong cheng xue za zhi = Journal of biomedical engineering = Shengwu yixue gongchengxue zazhi, 2018. 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
–field-weighted citation impact
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.

  1. [Optic cup and disc segmentation model based on linear attention and dual attention].Sheng wu yi xue gong cheng xue za zhi = Journal of biomedical engineering = Shengwu yixue gongchengxue zazhi · 2023
    Article
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

4 authors.

Xinrong CaoCollege of Physics and Information Engineering, Fuzhou University, Fuzhou 350116, P.R.China;Fujian Provincial Key Laboratory of Information Processing and Intelligent Control, Minjiang University, Fuzhou 350121, P.R.China.cxrxmu@163.com.
Lanyan XueCollege of Physics and Information Engineering, Fuzhou University, Fuzhou 350116, P.R.China.
Jiawen LinCollege of Physics and Information Engineering, Fuzhou University, Fuzhou 350116, P.R.China.
Lun YuCollege of Physics and Information Engineering, Fuzhou University, Fuzhou 350116, P.R.China.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Fast optic disk localization and boundary segmentation is an important research topic in computer aided diagnosis. This paper proposes a novel method to effectively segment optic disk by using human visual characteristics in analyzing and processing fundus image. After a general analysis of optic disk features in fundus images, the target of interest could be located quickly, and intensity, color and spatial distribution of the disc are used to generate saliency map based on pixel distance. Then the adaptive threshold is used to segment optic disk. Moreover, to reduce the influence of vascular, a rotary scanning method is devised to achieve complete and continuous contour of optic disk boundary. Tests in the public fundus images database Drishti-GS have good performances, which mean that the proposed method is simple and rapid, and it meets the standard of the eye specialists. It is hoped that the method could be conducive to the computer aided diagnosis of eye diseases in the future.

Indexed as

optic disk segmentationrotary scanningvisual saliency

Identifiers

PMID29745528
PMCPMC9935090

What OpenQuestion holds

Textmetadata
Read underepoch 390

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.