Evidence map›Paper›PMID 35585672›Full record

ArticleJournal of biomedical optics2022

No-reference image quality assessment for confocal endoscopy images with perceptual local descriptor.

Xiangjiang Dong, Ling Fu, Qian Liu

Abstract read
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Article in Journal of biomedical optics, 2022. 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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1 · What the graph read from it

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2 · The registry

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3 · Its place in the literature

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4 · The record

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5 · Who and what money

Authors and funding

3 authors.

Xiangjiang DongHuazhong University of Science and Technology, Wuhan National Laboratory for Optoelectronics, Wuhan, China.
Ling FuHuazhong University of Science and Technology, Wuhan National Laboratory for Optoelectronics, Wuhan, China.
Qian LiuHainan University, School of Biomedical Engineering, Key Laboratory of Biomedical Engineering of Hai, China.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

significanceConfocal endoscopy images often suffer distortions, resulting in image quality degradation and information loss, increasing the difficulty of diagnosis and even leading to misdiagnosis. It is important to assess image quality and filter images with low diagnostic value before diagnosis.

aimWe propose a no-reference image quality assessment (IQA) method for confocal endoscopy images based on Weber's law and local descriptors. The proposed method can detect the severity of image degradation by capturing the perceptual structure of an image. APPROACH: We created a new dataset of 642 confocal endoscopy images to validate the performance of the proposed method. We then conducted extensive experiments to compare the accuracy and speed of the proposed method with other state-of-the-art IQA methods.

resultsExperimental results demonstrate that the proposed method achieved an SROCC of 0.85 and outperformed other IQA methods.

conclusionsGiven its high consistency in subjective quality assessment, the proposed method can screen high-quality images in practical applications and contribute to diagnosis.

Indexed as

AlgorithmsImage Processing, Computer-AssistedEndoscopyconfocal endoscopydifferential excitationhuman visual systemimage quality assessmentlocal binary pattern

Identifiers

PMID35585672
PMCPMC9116465

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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.