| Title |
Statistical Geometrical Features for Microaneurysm Detection
|
|---|---|
| Published in |
Journal of Digital Imaging, August 2017
|
| DOI | 10.1007/s10278-017-0008-0 |
| Pubmed ID | |
| Authors |
Arati Manjaramkar, Manesh Kokare |
| Abstract |
Automated microaneurysm (MA) detection is still an open challenge due to its small size and similarity with blood vessels. In this paper, we present a novel method which is simple, efficient, and real-time for segmenting and detecting MA in color fundus images (CFI). To do this, a novel set of features based on statistics of geometrical properties of connected regions, that can easily discriminate lesion and non-lesion pixels are used. For large-scale evaluation proposed method is validated on DIARETDB1, ROC, STARE, and MESSIDOR dataset. It proves robust with respect to different image characteristics and camera settings. The best performance was achieved on per-image evaluation on DIARETDB1 dataset with sensitivity of 88.09 at 92.65% specificity which is quite encouraging for clinical use. |
X Demographics
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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 25 Mendeley readers of this research output. Click here to see the associated Mendeley record.
Geographical breakdown
| Country | Count | As % |
|---|---|---|
| Unknown | 25 | 100% |
Demographic breakdown
| Readers by professional status | Count | As % |
|---|---|---|
| Researcher | 3 | 12% |
| Student > Master | 3 | 12% |
| Lecturer | 2 | 8% |
| Professor | 2 | 8% |
| Student > Ph. D. Student | 2 | 8% |
| Other | 3 | 12% |
| Unknown | 10 | 40% |
| Readers by discipline | Count | As % |
|---|---|---|
| Computer Science | 5 | 20% |
| Engineering | 3 | 12% |
| Medicine and Dentistry | 2 | 8% |
| Mathematics | 1 | 4% |
| Chemistry | 1 | 4% |
| Other | 1 | 4% |
| Unknown | 12 | 48% |
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 11 April 2018.
All research outputs
#20,480,611
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Outputs from Journal of Digital Imaging
#945
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Outputs of similar age
#277,220
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Outputs of similar age from Journal of Digital Imaging
#25
of 29 outputs
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