| Title |
Automatic screening and classification of diabetic retinopathy and maculopathy using fuzzy image processing
|
|---|---|
| Published in |
Brain Informatics, March 2016
|
| DOI | 10.1007/s40708-016-0045-3 |
| Pubmed ID | |
| Authors |
Sarni Suhaila Rahim, Vasile Palade, James Shuttleworth, Chrisina Jayne |
| Abstract |
Digital retinal imaging is a challenging screening method for which effective, robust and cost-effective approaches are still to be developed. Regular screening for diabetic retinopathy and diabetic maculopathy diseases is necessary in order to identify the group at risk of visual impairment. This paper presents a novel automatic detection of diabetic retinopathy and maculopathy in eye fundus images by employing fuzzy image processing techniques. The paper first introduces the existing systems for diabetic retinopathy screening, with an emphasis on the maculopathy detection methods. The proposed medical decision support system consists of four parts, namely: image acquisition, image preprocessing including four retinal structures localisation, feature extraction and the classification of diabetic retinopathy and maculopathy. A combination of fuzzy image processing techniques, the Circular Hough Transform and several feature extraction methods are implemented in the proposed system. The paper also presents a novel technique for the macula region localisation in order to detect the maculopathy. In addition to the proposed detection system, the paper highlights a novel online dataset and it presents the dataset collection, the expert diagnosis process and the advantages of our online database compared to other public eye fundus image databases for diabetic retinopathy purposes. |
X Demographics
The data shown below were collected from the profile of 1 X user who shared this research output. Click here to find out more about how the information was compiled.
Geographical breakdown
| Country | Count | As % |
|---|---|---|
| Indonesia | 1 | 100% |
Demographic breakdown
| Type | Count | As % |
|---|---|---|
| Members of the public | 1 | 100% |
Mendeley readers
The data shown below were compiled from readership statistics for 91 Mendeley readers of this research output. Click here to see the associated Mendeley record.
Geographical breakdown
| Country | Count | As % |
|---|---|---|
| Unknown | 91 | 100% |
Demographic breakdown
| Readers by professional status | Count | As % |
|---|---|---|
| Student > Bachelor | 13 | 14% |
| Student > Ph. D. Student | 9 | 10% |
| Student > Master | 8 | 9% |
| Researcher | 7 | 8% |
| Lecturer | 5 | 5% |
| Other | 14 | 15% |
| Unknown | 35 | 38% |
| Readers by discipline | Count | As % |
|---|---|---|
| Computer Science | 24 | 26% |
| Engineering | 18 | 20% |
| Medicine and Dentistry | 3 | 3% |
| Agricultural and Biological Sciences | 1 | 1% |
| Biochemistry, Genetics and Molecular Biology | 1 | 1% |
| Other | 4 | 4% |
| Unknown | 40 | 44% |
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 27 March 2016.
All research outputs
#15,365,885
of 22,858,915 outputs
Outputs from Brain Informatics
#67
of 103 outputs
Outputs of similar age
#179,108
of 300,004 outputs
Outputs of similar age from Brain Informatics
#11
of 15 outputs
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