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Semi-automated quantification of hard exudates in colour fundus photographs diagnosed with diabetic retinopathy

Overview of attention for article published in BMC Ophthalmology, September 2017
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Title
Semi-automated quantification of hard exudates in colour fundus photographs diagnosed with diabetic retinopathy
Published in
BMC Ophthalmology, September 2017
DOI 10.1186/s12886-017-0563-7
Pubmed ID
Authors

Abhilash Goud Marupally, Kiran Kumar Vupparaboina, Hari Kumar Peguda, Ashutosh Richhariya, Soumya Jana, Jay Chhablani

Abstract

Hard exudates (HEs) are the classical sign of diabetic retinopathy (DR) which is one of the leading causes of blindness, especially in developing countries. Accordingly, disease screening involves examining HEs qualitatively using fundus camera. However, for monitoring the treatment response, quantification of HEs becomes crucial and hence clinicians now seek to measure the area of HEs in the digital colour fundus (CF) photographs. Against this backdrop, we proposed an algorithm to quantify HEs using CF images and compare with previously reported technique using ImageJ. CF photographs of 30 eyes (20 patients) with diabetic macular edema were obtained. A robust semi-automated algorithm was developed to quantify area covered by HEs. In particular, the proposed algorithm, a two pronged methodology, involved performing top-hat filtering, second order statistical filtering, and thresholding of the colour fundus images. Subsequently, two masked observers performed HEs measurements using previously reported ImageJ-based protocol and compared with those obtained through proposed method. Intra and inter-observer grading was performed for determining percentage area of HEs identified by the individual algorithm. Of the 30 subjects, 21 were males and 9 were females with a mean age of the 50.25 ± 7.80 years (range 33-66 years). The correlation between the two measurements of semi-automated and ImageJ were 0.99 and 0.99 respectively. Previously reported method detected only 0-30% of the HEs area in 9 images, 30-60% in 12 images and 60-90% in remaining images, and more than 90% in none. In contrast, proposed method, detected 60-90% of the HEs area in 13 images and 90-100% in remaining 17 images. Proposed method semi-automated algorithm achieved acceptable accuracy, qualitatively and quantitatively, on a heterogeneous dataset. Further, quantitative analysis performed based on intra- and inter-observer grading showed that proposed methodology detects HEs more accurately than previously reported ImageJ-based technique. In particular, we proposed algorithm detect faint HEs also as opposed to the earlier method.

Twitter Demographics

The data shown below were collected from the profiles of 2 tweeters who shared this research output. Click here to find out more about how the information was compiled.

Mendeley readers

The data shown below were compiled from readership statistics for 10 Mendeley readers of this research output. Click here to see the associated Mendeley record.

Geographical breakdown

Country Count As %
Unknown 10 100%

Demographic breakdown

Readers by professional status Count As %
Researcher 3 30%
Lecturer > Senior Lecturer 2 20%
Student > Ph. D. Student 2 20%
Lecturer 1 10%
Unspecified 1 10%
Other 1 10%
Readers by discipline Count As %
Medicine and Dentistry 3 30%
Computer Science 2 20%
Engineering 2 20%
Unspecified 2 20%
Business, Management and Accounting 1 10%
Other 0 0%

Attention Score in Context

This research output has an Altmetric Attention Score of 2. 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 21 September 2017.
All research outputs
#7,323,672
of 11,805,285 outputs
Outputs from BMC Ophthalmology
#246
of 624 outputs
Outputs of similar age
#151,610
of 267,888 outputs
Outputs of similar age from BMC Ophthalmology
#5
of 12 outputs
Altmetric has tracked 11,805,285 research outputs across all sources so far. This one is in the 37th percentile – i.e., 37% of other outputs scored the same or lower than it.
So far Altmetric has tracked 624 research outputs from this source. They receive a mean Attention Score of 2.3. This one has gotten more attention than average, scoring higher than 60% of its peers.
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 267,888 tracked outputs that were published within six weeks on either side of this one in any source. This one is in the 42nd percentile – i.e., 42% of its contemporaries scored the same or lower than it.
We're also able to compare this research output to 12 others from the same source and published within six weeks on either side of this one. This one has gotten more attention than average, scoring higher than 58% of its contemporaries.