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Retinal status analysis method based on feature extraction and quantitative grading in OCT images

Overview of attention for article published in BioMedical Engineering OnLine, July 2016
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2 X users

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Title
Retinal status analysis method based on feature extraction and quantitative grading in OCT images
Published in
BioMedical Engineering OnLine, July 2016
DOI 10.1186/s12938-016-0206-x
Pubmed ID
Authors

Dongmei Fu, Hejun Tong, Shuang Zheng, Ling Luo, Fulin Gao, Jiri Minar

Abstract

Optical coherence tomography (OCT) is widely used in ophthalmology for viewing the morphology of the retina, which is important for disease detection and assessing therapeutic effect. The diagnosis of retinal diseases is based primarily on the subjective analysis of OCT images by trained ophthalmologists. This paper describes an OCT images automatic analysis method for computer-aided disease diagnosis and it is a critical part of the eye fundus diagnosis. This study analyzed 300 OCT images acquired by Optovue Avanti RTVue XR (Optovue Corp., Fremont, CA). Firstly, the normal retinal reference model based on retinal boundaries was presented. Subsequently, two kinds of quantitative methods based on geometric features and morphological features were proposed. This paper put forward a retinal abnormal grading decision-making method which was used in actual analysis and evaluation of multiple OCT images. This paper showed detailed analysis process by four retinal OCT images with different abnormal degrees. The final grading results verified that the analysis method can distinguish abnormal severity and lesion regions. This paper presented the simulation of the 150 test images, where the results of analysis of retinal status showed that the sensitivity was 0.94 and specificity was 0.92.The proposed method can speed up diagnostic process and objectively evaluate the retinal status. This paper aims on studies of retinal status automatic analysis method based on feature extraction and quantitative grading in OCT images. The proposed method can obtain the parameters and the features that are associated with retinal morphology. Quantitative analysis and evaluation of these features are combined with reference model which can realize the target image abnormal judgment and provide a reference for disease diagnosis.

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Mendeley readers

Mendeley readers

The data shown below were compiled from readership statistics for 26 Mendeley readers of this research output. Click here to see the associated Mendeley record.

Geographical breakdown

Country Count As %
Unknown 26 100%

Demographic breakdown

Readers by professional status Count As %
Student > Ph. D. Student 4 15%
Researcher 4 15%
Student > Bachelor 3 12%
Student > Postgraduate 2 8%
Student > Master 1 4%
Other 2 8%
Unknown 10 38%
Readers by discipline Count As %
Medicine and Dentistry 7 27%
Engineering 3 12%
Nursing and Health Professions 2 8%
Chemistry 2 8%
Computer Science 1 4%
Other 1 4%
Unknown 10 38%
Attention Score in Context

Attention Score in Context

This research output has an Altmetric Attention Score of 1. This is our high-level measure of the quality and quantity of online attention that it has received. This Attention Score, as well as the ranking and number of research outputs shown below, was calculated when the research output was last mentioned on 22 August 2016.
All research outputs
#19,076,161
of 24,292,134 outputs
Outputs from BioMedical Engineering OnLine
#560
of 843 outputs
Outputs of similar age
#274,282
of 371,138 outputs
Outputs of similar age from BioMedical Engineering OnLine
#13
of 14 outputs
Altmetric has tracked 24,292,134 research outputs across all sources so far. This one is in the 18th percentile – i.e., 18% of other outputs scored the same or lower than it.
So far Altmetric has tracked 843 research outputs from this source. They typically receive a little more attention than average, with a mean Attention Score of 5.1. This one is in the 30th percentile – i.e., 30% of its peers scored the same or lower than it.
Older research outputs will score higher simply because they've had more time to accumulate mentions. To account for age we can compare this Altmetric Attention Score to the 371,138 tracked outputs that were published within six weeks on either side of this one in any source. This one is in the 22nd percentile – i.e., 22% of its contemporaries scored the same or lower than it.
We're also able to compare this research output to 14 others from the same source and published within six weeks on either side of this one. This one is in the 7th percentile – i.e., 7% of its contemporaries scored the same or lower than it.