| Title |
An Unsupervised Approach for Extraction of Blood Vessels from Fundus Images
|
|---|---|
| Published in |
Journal of Digital Imaging, April 2018
|
| DOI | 10.1007/s10278-018-0059-x |
| Pubmed ID | |
| Authors |
Jyotiprava Dash, Nilamani Bhoi |
| Abstract |
Pathological disorders may happen due to small changes in retinal blood vessels which may later turn into blindness. Hence, the accurate segmentation of blood vessels is becoming a challenging task for pathological analysis. This paper offers an unsupervised recursive method for extraction of blood vessels from ophthalmoscope images. First, a vessel-enhanced image is generated with the help of gamma correction and contrast-limited adaptive histogram equalization (CLAHE). Next, the vessels are extracted iteratively by applying an adaptive thresholding technique. At last, a final vessel segmented image is produced by applying a morphological cleaning operation. Evaluations are accompanied on the publicly available digital retinal images for vessel extraction (DRIVE) and Child Heart And Health Study in England (CHASE_DB1) databases using nine different measurements. The proposed method achieves average accuracies of 0.957 and 0.952 on DRIVE and CHASE_DB1 databases respectively. |
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Geographical breakdown
| Country | Count | As % |
|---|---|---|
| Unknown | 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 31 Mendeley readers of this research output. Click here to see the associated Mendeley record.
Geographical breakdown
| Country | Count | As % |
|---|---|---|
| Unknown | 31 | 100% |
Demographic breakdown
| Readers by professional status | Count | As % |
|---|---|---|
| Student > Ph. D. Student | 7 | 23% |
| Other | 4 | 13% |
| Researcher | 3 | 10% |
| Lecturer | 2 | 6% |
| Student > Master | 2 | 6% |
| Other | 4 | 13% |
| Unknown | 9 | 29% |
| Readers by discipline | Count | As % |
|---|---|---|
| Computer Science | 12 | 39% |
| Engineering | 4 | 13% |
| Nursing and Health Professions | 1 | 3% |
| Psychology | 1 | 3% |
| Agricultural and Biological Sciences | 1 | 3% |
| Other | 2 | 6% |
| Unknown | 10 | 32% |
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 14 May 2018.
All research outputs
#20,489,895
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Outputs from Journal of Digital Imaging
#945
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Outputs of similar age
#287,643
of 326,642 outputs
Outputs of similar age from Journal of Digital Imaging
#24
of 30 outputs
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