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Hybrid Canny Zerocross Method for Edge Detection in Retina Identification Cases

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@article{IJASEIT17229,
   author = {Silfia Andini and Anjar Wanto and Retno Devita and Ruri Hartika Zain and Aulia Fitrul Hadi},
   title = {Hybrid Canny Zerocross Method for Edge Detection in Retina Identification Cases},
   journal = {International Journal on Advanced Science, Engineering and Information Technology},
   volume = {12},
   number = {4},
   year = {2022},
   pages = {1379--1386},
   keywords = {Edge detection; hybrid; canny; Zerocross; Retina.},
   abstract = {Edge detection is fundamental to Figure processing. Edges include much information in a figure, including the object's location, shape, size, and information about its texture. Since edge detection is a critical component of Figure processing for object detection, comprehend algorithms for edge detection. This is because the edges define an item's contours, serve as a demarcation between the object and its backdrop, and serve as a demarcation between overlapping objects. That is, if the edges of an image can be identified accurately, all things can be found. The proposal of this paper is the use of the Canny Zerocross hybrid method to perform better edge detection based on comparative studies and the incorporation of the Canny way, which is considered one of the best edge detection methods, with the Zerocross way (cross zero) which is a derivative of the laplacian. In this paper, the research data used is the retinal image dataset—data obtained from STARE (Structured Analysis of the Retina). The Veterans Administration Medical Center in San Diego and the Shiley Eye Center (ECS) at the University of California provided Figures and clinical data from the retinal images. The experimental results of the comparative study show that the Zerocross edge detection technique is better than the Canny edge detection technique. Meanwhile, edge detection and image identification would be better when combining the two methods (hybrid) based on merging studies.},
   issn = {2088-5334},
   publisher = {INSIGHT - Indonesian Society for Knowledge and Human Development},
   url = {http://ijaseit.insightsociety.org/index.php?option=com_content&view=article&id=9&Itemid=1&article_id=17229},
   doi = {10.18517/ijaseit.12.4.17229}
}

EndNote

%A Andini, Silfia
%A Wanto, Anjar
%A Devita, Retno
%A Zain, Ruri Hartika
%A Hadi, Aulia Fitrul
%D 2022
%T Hybrid Canny Zerocross Method for Edge Detection in Retina Identification Cases
%B 2022
%9 Edge detection; hybrid; canny; Zerocross; Retina.
%! Hybrid Canny Zerocross Method for Edge Detection in Retina Identification Cases
%K Edge detection; hybrid; canny; Zerocross; Retina.
%X Edge detection is fundamental to Figure processing. Edges include much information in a figure, including the object's location, shape, size, and information about its texture. Since edge detection is a critical component of Figure processing for object detection, comprehend algorithms for edge detection. This is because the edges define an item's contours, serve as a demarcation between the object and its backdrop, and serve as a demarcation between overlapping objects. That is, if the edges of an image can be identified accurately, all things can be found. The proposal of this paper is the use of the Canny Zerocross hybrid method to perform better edge detection based on comparative studies and the incorporation of the Canny way, which is considered one of the best edge detection methods, with the Zerocross way (cross zero) which is a derivative of the laplacian. In this paper, the research data used is the retinal image dataset—data obtained from STARE (Structured Analysis of the Retina). The Veterans Administration Medical Center in San Diego and the Shiley Eye Center (ECS) at the University of California provided Figures and clinical data from the retinal images. The experimental results of the comparative study show that the Zerocross edge detection technique is better than the Canny edge detection technique. Meanwhile, edge detection and image identification would be better when combining the two methods (hybrid) based on merging studies.
%U http://ijaseit.insightsociety.org/index.php?option=com_content&view=article&id=9&Itemid=1&article_id=17229
%R doi:10.18517/ijaseit.12.4.17229
%J International Journal on Advanced Science, Engineering and Information Technology
%V 12
%N 4
%@ 2088-5334

IEEE

Silfia Andini,Anjar Wanto,Retno Devita,Ruri Hartika Zain and Aulia Fitrul Hadi,"Hybrid Canny Zerocross Method for Edge Detection in Retina Identification Cases," International Journal on Advanced Science, Engineering and Information Technology, vol. 12, no. 4, pp. 1379-1386, 2022. [Online]. Available: http://dx.doi.org/10.18517/ijaseit.12.4.17229.

RefMan/ProCite (RIS)

TY  - JOUR
AU  - Andini, Silfia
AU  - Wanto, Anjar
AU  - Devita, Retno
AU  - Zain, Ruri Hartika
AU  - Hadi, Aulia Fitrul
PY  - 2022
TI  - Hybrid Canny Zerocross Method for Edge Detection in Retina Identification Cases
JF  - International Journal on Advanced Science, Engineering and Information Technology; Vol. 12 (2022) No. 4
Y2  - 2022
SP  - 1379
EP  - 1386
SN  - 2088-5334
PB  - INSIGHT - Indonesian Society for Knowledge and Human Development
KW  - Edge detection; hybrid; canny; Zerocross; Retina.
N2  - Edge detection is fundamental to Figure processing. Edges include much information in a figure, including the object's location, shape, size, and information about its texture. Since edge detection is a critical component of Figure processing for object detection, comprehend algorithms for edge detection. This is because the edges define an item's contours, serve as a demarcation between the object and its backdrop, and serve as a demarcation between overlapping objects. That is, if the edges of an image can be identified accurately, all things can be found. The proposal of this paper is the use of the Canny Zerocross hybrid method to perform better edge detection based on comparative studies and the incorporation of the Canny way, which is considered one of the best edge detection methods, with the Zerocross way (cross zero) which is a derivative of the laplacian. In this paper, the research data used is the retinal image dataset—data obtained from STARE (Structured Analysis of the Retina). The Veterans Administration Medical Center in San Diego and the Shiley Eye Center (ECS) at the University of California provided Figures and clinical data from the retinal images. The experimental results of the comparative study show that the Zerocross edge detection technique is better than the Canny edge detection technique. Meanwhile, edge detection and image identification would be better when combining the two methods (hybrid) based on merging studies.
UR  - http://ijaseit.insightsociety.org/index.php?option=com_content&view=article&id=9&Itemid=1&article_id=17229
DO  - 10.18517/ijaseit.12.4.17229

RefWorks

RT Journal Article
ID 17229
A1 Andini, Silfia
A1 Wanto, Anjar
A1 Devita, Retno
A1 Zain, Ruri Hartika
A1 Hadi, Aulia Fitrul
T1 Hybrid Canny Zerocross Method for Edge Detection in Retina Identification Cases
JF International Journal on Advanced Science, Engineering and Information Technology
VO 12
IS 4
YR 2022
SP 1379
OP 1386
SN 2088-5334
PB INSIGHT - Indonesian Society for Knowledge and Human Development
K1 Edge detection; hybrid; canny; Zerocross; Retina.
AB Edge detection is fundamental to Figure processing. Edges include much information in a figure, including the object's location, shape, size, and information about its texture. Since edge detection is a critical component of Figure processing for object detection, comprehend algorithms for edge detection. This is because the edges define an item's contours, serve as a demarcation between the object and its backdrop, and serve as a demarcation between overlapping objects. That is, if the edges of an image can be identified accurately, all things can be found. The proposal of this paper is the use of the Canny Zerocross hybrid method to perform better edge detection based on comparative studies and the incorporation of the Canny way, which is considered one of the best edge detection methods, with the Zerocross way (cross zero) which is a derivative of the laplacian. In this paper, the research data used is the retinal image dataset—data obtained from STARE (Structured Analysis of the Retina). The Veterans Administration Medical Center in San Diego and the Shiley Eye Center (ECS) at the University of California provided Figures and clinical data from the retinal images. The experimental results of the comparative study show that the Zerocross edge detection technique is better than the Canny edge detection technique. Meanwhile, edge detection and image identification would be better when combining the two methods (hybrid) based on merging studies.
LK http://ijaseit.insightsociety.org/index.php?option=com_content&view=article&id=9&Itemid=1&article_id=17229
DO  - 10.18517/ijaseit.12.4.17229