Morphological and Otsu Thresholding Based Retinal Blood Vessel Segmentation for Detection of Retinopathy.
Purpose: Retinal blood vessel segmentation is crucial as it is the earliest process in measuring various indicators of retinopathy sign such as arterial-venous nicking, and focal arteriolar and generalized arteriolar narrowing. The segmentation can be clinically used if its accuracy is close to 100%...
| Main Authors: | , , , , , , , |
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| Format: | Proceeding |
| Language: | English |
| Published: |
2017
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| Online Access: | http://ir.unimas.my/id/eprint/19085/ http://ir.unimas.my/id/eprint/19085/1/Retinal%20Image%20Segmentation%20Kuryati%20USJC_Formatted%20-%20%28abstrak%29.pdf |
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| author | Kipli, Kuryati Jiris, Cripen Sahari, Siti Kudnie Sapawi, Rohana Junaidi, Nazreen Sapawi, Marini Hong Ping, Kismet Zulcaffle, Tengku Mohd Affendi |
| author_facet | Kipli, Kuryati Jiris, Cripen Sahari, Siti Kudnie Sapawi, Rohana Junaidi, Nazreen Sapawi, Marini Hong Ping, Kismet Zulcaffle, Tengku Mohd Affendi |
| author_sort | Kipli, Kuryati |
| building | UNIMAS Institutional Repository |
| collection | Online Access |
| description | Purpose: Retinal blood vessel segmentation is crucial as it is the earliest process in measuring various indicators of retinopathy sign such as arterial-venous nicking, and focal arteriolar and generalized arteriolar narrowing. The segmentation can be clinically used if its accuracy is close to 100%. In this study, a new method of segmentation is developed for extraction of retinal blood vessel.
Methods: In this paper, we present a new automated method to extract blood vessels in retinal fundus images. The proposed method comprises of two main parts and a few subcomponents which include pre-processing and segmentation. The main focus for the segmentation part is two morphological reconstructions which are the morphological reconstructions followed by the morphological top-hat transform. Then the technique to classify the vessel pixels and background pixels is Otsu’s thresholding. The image database used in this study is the High Resolution Fundus Image Database (HRFID).
Results: The developed segmentation method accuracy are 95.17%, 92.06% and 94.71% when tested on dataset of healthy, diabetic retinopathy (DR) and glaucoma patients respectively.
Conclusion: Overall, the performance of the proposed method is comparable with existing methods with overall accuracies were more than 90 % for all three different categories: healthy, DR and glaucoma |
| first_indexed | 2025-11-15T06:58:45Z |
| format | Proceeding |
| id | unimas-19085 |
| institution | Universiti Malaysia Sarawak |
| institution_category | Local University |
| language | English |
| last_indexed | 2025-11-15T06:58:45Z |
| publishDate | 2017 |
| recordtype | eprints |
| repository_type | Digital Repository |
| spelling | unimas-190852017-12-29T06:45:31Z http://ir.unimas.my/id/eprint/19085/ Morphological and Otsu Thresholding Based Retinal Blood Vessel Segmentation for Detection of Retinopathy. Kipli, Kuryati Jiris, Cripen Sahari, Siti Kudnie Sapawi, Rohana Junaidi, Nazreen Sapawi, Marini Hong Ping, Kismet Zulcaffle, Tengku Mohd Affendi QA75 Electronic computers. Computer science Purpose: Retinal blood vessel segmentation is crucial as it is the earliest process in measuring various indicators of retinopathy sign such as arterial-venous nicking, and focal arteriolar and generalized arteriolar narrowing. The segmentation can be clinically used if its accuracy is close to 100%. In this study, a new method of segmentation is developed for extraction of retinal blood vessel. Methods: In this paper, we present a new automated method to extract blood vessels in retinal fundus images. The proposed method comprises of two main parts and a few subcomponents which include pre-processing and segmentation. The main focus for the segmentation part is two morphological reconstructions which are the morphological reconstructions followed by the morphological top-hat transform. Then the technique to classify the vessel pixels and background pixels is Otsu’s thresholding. The image database used in this study is the High Resolution Fundus Image Database (HRFID). Results: The developed segmentation method accuracy are 95.17%, 92.06% and 94.71% when tested on dataset of healthy, diabetic retinopathy (DR) and glaucoma patients respectively. Conclusion: Overall, the performance of the proposed method is comparable with existing methods with overall accuracies were more than 90 % for all three different categories: healthy, DR and glaucoma 2017-10-20 Proceeding PeerReviewed text en http://ir.unimas.my/id/eprint/19085/1/Retinal%20Image%20Segmentation%20Kuryati%20USJC_Formatted%20-%20%28abstrak%29.pdf Kipli, Kuryati and Jiris, Cripen and Sahari, Siti Kudnie and Sapawi, Rohana and Junaidi, Nazreen and Sapawi, Marini and Hong Ping, Kismet and Zulcaffle, Tengku Mohd Affendi (2017) Morphological and Otsu Thresholding Based Retinal Blood Vessel Segmentation for Detection of Retinopathy. In: UNIMAS SILVER JUBILEE CONFERENCE 2017 (USJC 2017), 18-20 October 2017, Pullman Kuching. |
| spellingShingle | QA75 Electronic computers. Computer science Kipli, Kuryati Jiris, Cripen Sahari, Siti Kudnie Sapawi, Rohana Junaidi, Nazreen Sapawi, Marini Hong Ping, Kismet Zulcaffle, Tengku Mohd Affendi Morphological and Otsu Thresholding Based Retinal Blood Vessel Segmentation for Detection of Retinopathy. |
| title | Morphological and Otsu Thresholding Based Retinal Blood Vessel Segmentation for Detection of Retinopathy. |
| title_full | Morphological and Otsu Thresholding Based Retinal Blood Vessel Segmentation for Detection of Retinopathy. |
| title_fullStr | Morphological and Otsu Thresholding Based Retinal Blood Vessel Segmentation for Detection of Retinopathy. |
| title_full_unstemmed | Morphological and Otsu Thresholding Based Retinal Blood Vessel Segmentation for Detection of Retinopathy. |
| title_short | Morphological and Otsu Thresholding Based Retinal Blood Vessel Segmentation for Detection of Retinopathy. |
| title_sort | morphological and otsu thresholding based retinal blood vessel segmentation for detection of retinopathy. |
| topic | QA75 Electronic computers. Computer science |
| url | http://ir.unimas.my/id/eprint/19085/ http://ir.unimas.my/id/eprint/19085/1/Retinal%20Image%20Segmentation%20Kuryati%20USJC_Formatted%20-%20%28abstrak%29.pdf |