dc.contributor.author |
Oluwatobi, JA
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dc.contributor.author |
Mabuza-Hocquet, Gugulethu P
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dc.contributor.author |
Nelwamondo, Fulufhelo V
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dc.date.accessioned |
2020-12-09T10:56:54Z |
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dc.date.available |
2020-12-09T10:56:54Z |
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dc.date.issued |
2019-12 |
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dc.identifier.citation |
Oluwatobi, J.A., Mabuza-Hocquet, G.P. and Nelwamondo, F.V. 2019. Assessment of the ISNT rule on publicly available datasets. 19th International Conference on Intelligent Systems Design and Applications, Pretoria, South Africa, 3-5 December 2019 |
en_US |
dc.identifier.isbn |
978-3-030-49341-7 |
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dc.identifier.isbn |
978-3-030-49342-4 |
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dc.identifier.uri |
https://link.springer.com/chapter/10.1007/978-3-030-49342-4_27
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dc.identifier.uri |
DOI: https://doi.org/10.1007/978-3-030-49342-4_27
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dc.identifier.uri |
http://hdl.handle.net/10204/11694
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dc.description |
© The Editor(s) (if applicable) and The Author(s), under exclusive license to Springer Nature Switzerland AG 2021. The attached pdf is the accepted version of the published paper. For access to the the published version, kindly visit https://link.springer.com/chapter/10.1007/978-3-030-49342-4_27 |
en_US |
dc.description.abstract |
The ISNT rule is a technique that has been used to detect glaucoma from fundus im-ages. The rule states that for a healthy fundus image, the segmented optic disc can be divided into four neuro-retina rim quadrants namely; the Inferior, Superior, Nasal and Temporal neuro- retina rims. The Inferior is the widest followed by the Superior then the Nasal. The Temporal quadrant is the least. However, since the advent of the rule there have been several experiments that prove the inefficiency of the rule to diagnose glaucoma while other experiments argue that the rule is efficient. Experiments carried out by individuals were done using dataset sourced by the individuals not on publicly available fundus datasets. This makes the experiments not easily reproducible. This work assesses the ISNT rule using the RIM-ONE v3 dataset and the DRISHTI-GS dataset which are both publicly available datasets. The performance of the ISNT rule on the datasets is compared with the performance of a trained Extreme Gradient Boost classifier (XGB). The results show that the XGB classifier outperforms the ISNT rule and its variant. The ISNT rule demonstrated a random performance on the databases used. |
en_US |
dc.language.iso |
en |
en_US |
dc.publisher |
Springer |
en_US |
dc.relation.ispartofseries |
Worklist;23036 |
|
dc.subject |
Retinal Fundus Image |
en_US |
dc.subject |
Glaucoma |
en_US |
dc.subject |
ISNT |
en_US |
dc.subject |
Blood vessel segmentation |
en_US |
dc.subject |
Image segmentation |
en_US |
dc.title |
Assessment of the ISNT rule on publicly available datasets |
en_US |
dc.type |
Conference Presentation |
en_US |
dc.identifier.apacitation |
Oluwatobi, J., Mabuza-Hocquet, G. P., & Nelwamondo, F. V. (2019). Assessment of the ISNT rule on publicly available datasets. Springer. http://hdl.handle.net/10204/11694 |
en_ZA |
dc.identifier.chicagocitation |
Oluwatobi, JA, Gugulethu P Mabuza-Hocquet, and Fulufhelo V Nelwamondo. "Assessment of the ISNT rule on publicly available datasets." (2019): http://hdl.handle.net/10204/11694 |
en_ZA |
dc.identifier.vancouvercitation |
Oluwatobi J, Mabuza-Hocquet GP, Nelwamondo FV, Assessment of the ISNT rule on publicly available datasets; Springer; 2019. http://hdl.handle.net/10204/11694 . |
en_ZA |
dc.identifier.ris |
TY - Conference Presentation
AU - Oluwatobi, JA
AU - Mabuza-Hocquet, Gugulethu P
AU - Nelwamondo, Fulufhelo V
AB - The ISNT rule is a technique that has been used to detect glaucoma from fundus im-ages. The rule states that for a healthy fundus image, the segmented optic disc can be divided into four neuro-retina rim quadrants namely; the Inferior, Superior, Nasal and Temporal neuro- retina rims. The Inferior is the widest followed by the Superior then the Nasal. The Temporal quadrant is the least. However, since the advent of the rule there have been several experiments that prove the inefficiency of the rule to diagnose glaucoma while other experiments argue that the rule is efficient. Experiments carried out by individuals were done using dataset sourced by the individuals not on publicly available fundus datasets. This makes the experiments not easily reproducible. This work assesses the ISNT rule using the RIM-ONE v3 dataset and the DRISHTI-GS dataset which are both publicly available datasets. The performance of the ISNT rule on the datasets is compared with the performance of a trained Extreme Gradient Boost classifier (XGB). The results show that the XGB classifier outperforms the ISNT rule and its variant. The ISNT rule demonstrated a random performance on the databases used.
DA - 2019-12
DB - ResearchSpace
DP - CSIR
KW - Retinal Fundus Image
KW - Glaucoma
KW - ISNT
KW - Blood vessel segmentation
KW - Image segmentation
LK - https://researchspace.csir.co.za
PY - 2019
SM - 978-3-030-49341-7
SM - 978-3-030-49342-4
T1 - Assessment of the ISNT rule on publicly available datasets
TI - Assessment of the ISNT rule on publicly available datasets
UR - http://hdl.handle.net/10204/11694
ER -
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en_ZA |