A comprehensive survey on segmentation techniques for retinal vessel segmentation

被引:18
作者
Cervantes, Jair [1 ]
Cervantes, Jared [1 ]
Garcia-Lamont, Farid [1 ]
Yee-Rendon, Arturo [2 ]
Cabrera, Josue Espejel [1 ]
Jalili, Laura Dominguez [1 ]
机构
[1] UAEMEX Autonomous Univ Mexico State, Texcoco 56259, Mexico
[2] Univ Autonoma Sinaloa, Fac Informat Culiacan, Culiacan 80013, Sinaloa, Mexico
关键词
Vessel segmentation; Preprocessing techniques; Segmentation techniques; CONVOLUTIONAL NEURAL-NETWORK; FUZZY C-MEANS; BLOOD-VESSELS; FUNDUS IMAGES; FEATURE-EXTRACTION; LEVEL SET; FUNCTION APPROXIMATION; PALMPRINT RECOGNITION; SPARSE REPRESENTATION; MATCHED-FILTER;
D O I
10.1016/j.neucom.2023.126626
中图分类号
TP18 [人工智能理论];
学科分类号
081104 ; 0812 ; 0835 ; 1405 ;
摘要
In recent years, enormous research has been carried out on the segmentation of blood vessels. Segmentation of blood vessels in retinal images is crucial for diagnosing, treating, evaluating clinical results, and early detection of eye disorders. A successful segmentation accurately reflects the blood vessels' structure and helps obtain patterns that can be used to identify retinal disorders and diseases. Most recent research on vessel segmentation employs multiple processes to determine the appropriate segmentation. Finding the best techniques for segmentation is a complex process. In certain circumstances, it requires a thorough understanding of every step that can only be acquired through years of training. Comprehension and expertise in segmentation procedures are essential for accurate segmentation. This paper briefly introduces the segmentation of blood vessels in retinal images, describes many preprocessing and segmentation techniques, and summarizes challenges and trends. Furthermore, the limitations of the current systems will be identified.
引用
收藏
页数:29
相关论文
共 248 条
[1]  
Aastha R.G., 2019, Int. J. Sci. Technol. Res., V8
[2]  
Abramoff M., 2013, Retina, P151
[3]   STATISTICAL-BASED LINEAR VESSEL STRUCTURE DETECTION IN MEDICAL IMAGES [J].
Adel, Mouloud ;
Rasigni, Monique ;
Gaidon, Thierry ;
Fossati, Caroline ;
Bourennane, Salah .
2009 16TH IEEE INTERNATIONAL CONFERENCE ON IMAGE PROCESSING, VOLS 1-6, 2009, :649-652
[4]   Blood vessel segmentation in retinal fundus images using Gabor filters, fractional derivatives, and Expectation Maximization [J].
Aguirre-Ramos, Hugo ;
Gabriel Avina-Cervantes, Juan ;
Cruz-Aceves, Ivan ;
Ruiz-Pinales, Jose ;
Ledesma, Sergio .
APPLIED MATHEMATICS AND COMPUTATION, 2018, 339 :568-587
[5]   Multilayered thresholding-based blood vessel segmentation for screening of diabetic retinopathy [J].
Akram, M. Usman ;
Khan, Shoab A. .
ENGINEERING WITH COMPUTERS, 2013, 29 (02) :165-173
[6]   REVIEW - A Reference Data Set for Retinal Vessel Profiles [J].
Al-Diri, Bashir ;
Hunter, Andrew ;
Steel, David ;
Habib, Maged ;
Hudaib, Taghread ;
Berry, Simon .
2008 30TH ANNUAL INTERNATIONAL CONFERENCE OF THE IEEE ENGINEERING IN MEDICINE AND BIOLOGY SOCIETY, VOLS 1-8, 2008, :2262-+
[7]   An Unsupervised Retinal Vessel Segmentation Using Hessian and Intensity Based Approach [J].
Alhussein, Musaed ;
Aurangzeb, Khursheed ;
Haider, Syed Irtaza .
IEEE ACCESS, 2020, 8 :165056-165070
[8]  
Ali O., 2020, 2020 3 INT C COMP MA, P1
[9]  
Alimanov A, 2022, Arxiv, DOI [arXiv:2209.04234, 10.48550/ARXIV.2209.04234]
[10]  
Alvarado-Carrillo Dora E., 2021, Geometry and Vision: First International Symposium, ISGV 2021. Communications in Computer and Information Science (1386), P378, DOI 10.1007/978-3-030-72073-5_29