Evidence map›Paper›PMID 34712289›Full record

ArticlePakistan journal of medical sciences2021

Construction and application of color fundus image segmentation algorithm based on Multi-Scale local combined global enhancement.

Yanjie Hao, Hongbo Xie, Rong Qiu

Abstract read
In one paragraph

Article in Pakistan journal of medical sciences, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.

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0citing papers in PubMed
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1 · What the graph read from it

What it found

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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

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3 · Its place in the literature

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0 citing papers in PubMed.

No citing paper in PubMed yet.

4 · The record

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5 · Who and what money

Authors and funding

3 authors.

Yanjie HaoYanjie Hao, Associate Chief Physician, Department of Ophthalmology, Jiaozhou Central Hospital of Qingdao, Qingdao, 266300, Shandong, China.
Hongbo XieHongbo Xie, Attending Physician, Department of Ophthalmology, Qingdao Women's and Children's Hospital, Qingdao, 266000, Shandong, China.
Rong QiuRong Qiu, Associate Chief Physician, Department of Ophthalmology, Zhucheng People's Hospital, Zhucheng, Shandong, China.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

objectiveAiming at the problem of low accuracy in extracting small blood vessels from existing retinal blood vessel images, a retinal blood vessel segmentation method based on a combination of a multi-scale linear detector and local and global enhancement is proposed.

methodsThe multi-scale line detector is studied, and it is divided into two parts: small scale and large scale. The small scale is used to detect the locally enhanced image and the large scale is used to detect the globally enhanced image. Fusion the response functions at different scales to get the final retinal vascular structure.

resultsExperiments on two databases STARE and DRIVE, show that the average vascular accuracy rates obtained by the algorithm reach 96.62% and 96.45%, and the average true positive rates reach 75.52% and 83.07%, respectively.

conclusionThe segmentation accuracy is high, and better blood vessel segmentation results can be obtained.

Indexed as

Blood vesselColor fundus image segmentation algorithmMulti-scale line detectorSegmentation accuracy

Identifiers

PMID34712289
PMCPMC8520383

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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.