A bio-inspired method for segmenting the optic disc and macula in retinal images

被引:3
作者
Azar, Ahmad Taher [1 ,2 ]
机构
[1] Prince Sultan Univ, Coll Comp Sci & Informat Syst, Riyadh, Saudi Arabia
[2] Benha Univ, Fac Comp & Artificial Intelligence, Banha, Egypt
关键词
ant colony system; bio-inspired algorithms; classification; retinal images; computer-aided diagnosis; DIABETIC-RETINOPATHY; VESSEL SEGMENTATION; AUTOMATIC DETECTION; BLOOD-VESSELS; DIAMETER; SYSTEM;
D O I
10.1504/IJCAT.2023.133882
中图分类号
TP39 [计算机的应用];
学科分类号
081203 ; 0835 ;
摘要
In this paper, Ant Colony System (ACS) based segmentation method is proposed and its features are used for segmenting the optic disc and macula in the retinal images. The suggested ACS-based segmentation approach employs intensity (gray-level) and colour as distinct characteristics to localise and segment the optic disc and macula in the retinal images. The evaluation and assessment of the performance of this nature-inspired approach for automatically separating blood vessels in retinal images by computer on standard retinal image databases revealed that it obtained the best separation of pixels that only belong to a blood vessel above all previous findings. The degree of accuracy of the proposed methodology approaches the best levels of accuracy achieved by techniques using supervised pixel classification algorithms.
引用
收藏
页码:262 / 277
页数:17
相关论文
共 63 条
[1]  
Asad Ahmed H., 2012, International Journal of Systems Biology and Biomedical Technologies, V1, P60, DOI 10.4018/ijsbbt.2012100105
[2]  
Asad A.H., 2014, Recent Advances in Intelligent Informatics, P1, DOI [10.1007/978-3-319-01778-5_1, DOI 10.1007/978-3-319-01778-5_1]
[3]  
Asad A.H., 2014, International Journal of Rough Sets and Data Analysis (IJRSDA), V1, P15
[4]  
Asad A.H., 2013, FED C COMP SCI INF S
[5]   Ant Colony-based System for Retinal Blood Vessels Segmentation [J].
Asad, Ahmed. H. ;
Azar, Ahmad Taher ;
Hassaanien, Aboul Ella .
PROCEEDINGS OF SEVENTH INTERNATIONAL CONFERENCE ON BIO-INSPIRED COMPUTING: THEORIES AND APPLICATIONS (BIC-TA 2012), VOL 1, 2013, 201 :441-+
[6]   Survey on recent developments in automatic detection of diabetic retinopathy [J].
Bilal, A. ;
Sun, G. ;
Mazhar, S. .
JOURNAL FRANCAIS D OPHTALMOLOGIE, 2021, 44 (03) :420-440
[7]  
Chakraborty S, 2022, INT J COMPUT APPL T, V68, P228, DOI [10.1504/IJCAT.2022.10049749, 10.1504/IJCAT.2022.124946]
[8]   Automatic detection of glaucoma via fundus imaging and artificial intelligence: A review [J].
Coan, Lauren J. ;
Williams, Bryan M. ;
Adithya, Venkatesh Krishna ;
Upadhyaya, Swati ;
Alkafri, Ala ;
Czanner, Silvester ;
Venkatesh, Rengaraj ;
Willoughby, Colin E. ;
Kavitha, Srinivasan ;
Czanner, Gabriela .
SURVEY OF OPHTHALMOLOGY, 2023, 68 (01) :17-41
[9]   Dermoscopic image segmentation method based on convolutional neural networks [J].
Dang Ngoc Hoang Thanh ;
Le Thi Thanh ;
Erkan, Ugur ;
Khamparia, Aditya ;
Prasath, V. B. Surya .
INTERNATIONAL JOURNAL OF COMPUTER APPLICATIONS IN TECHNOLOGY, 2021, 66 (02) :89-99
[10]  
Devi R.M., 2022, Machine Learning for Biometrics, Cognitive Data Science in Sustainable Computing