浏览全部资源
扫码关注微信
Received：07 May 2021，
Revised：27 October 2021，
Accepted：03 November 2021，
Published：16 October 2022
移动端阅览
青光眼是以视神经损伤、特征性视野损伤为特点的一类眼病，在早期很难诊断，尽早发现可更好地遏制青光眼病症的恶化，降低致盲率。视盘和视杯的比值是评价青光眼诊断中的重要指标之一，视盘和视杯的分割是青光眼诊断的关键步骤。但眼底彩照中的渗出物、不均匀照明区域等特征使其可能出现相似的亮度区域，导致视盘和视杯的分割非常困难。因此本文对现有眼底彩照中视盘和视杯的分割方法进行了总结，并将其分为5大类：水平集法、模态法、能量泛函法、划分法以及基于机器学习的混合法。系统地梳理了各类算法的代表性方法，以及基本思想、理论基础、关键技术、框架流程和优缺点等。同时，概括了适用于青光眼诊断的各种数据集，包括数据集的名称、来源以及详细内容，并总结了在各种数据集中不同视盘和视杯分割结果和诊断青光眼的量化指标及其相关结果。在现有的视盘和视杯分割方法中，许多图像处理和机器学习技术得到广泛应用。通过对该领域研究算法进行综述，清晰直观地总结了各类算法之间的特点及联系，有助于推动视盘和视杯分割在青光眼疾病临床诊断中的应用。可以在很大程度上提高临床医生的工作效率，为临床诊断青光眼提供了重要的理论研究意义和价值。
Glaucoma is a kind of human-related eyes disease derived from optic nerve and vision barrier. In most cases
the drainage system of human eyes is blocked and the liquid cannot be passed through
so the produced pressure will change the optic nerve in the eyes and lead to the collapse of the visual acuity. Although it is incurable
the progression of optic nerve injury can be preserved through intraocular pressure decreasing medication and surgery. Therefore
it is essential to prevent vision loss and even blindness for patients in early stage detection and timely treatment. In addition
the main manifestations of glaucoma are the enlargement of optic disc depression and the change of optic cup morphology
so the ratio of the optic disc to optic cup is one of the most important indexes in evaluating glaucoma screening. Nowadays
the segmentation of optic disc and optic cup has become an important part of the medical image field and has been widely concerned for a long time. However
the features of the fundus color make the potential to produce similar brightness areas
leading to the quite division difficulty of the optic disc and the optic cup. In the actual segmentation process
the accuracy and robustness of the optic disc and the optic cup segmentation can be guaranteed by dealing with the effects of the feature accurately and timely. Therefore
we summarized the existing methods of the optic disc and optic cup segmentation of retinal images. Our methods are divided into five categories like horizontal set
modal
energy functional
partition
and the hybrid contexts based on machine learning. At the same time
the machanism of each scheme are summarized and analyzed like the its basicconcepts
the theoretical basis
the key technologies
the framework flow
the advantages and the disadvantages. We carry out a detailed analysis and description like typical data sets
which are suitable for glaucoma diagnosis. Specifically
it is related to the name of the data set
the source
and the features of retinal images involved. To evaluate the segmentation results and the diagnosis of glaucoma
we faciliated the calculation methods of some important quantitative index parameters
such as cup-to-disc ratio(COR)
glaucoma risk index(GRI) and neural retinal edge ratio. Moreover
quantification index of a various of segmentation results of the optic disc and optic cup in multiple data sets (i.e.
relative area difference
overlap area ratio
and non-overlapping area ratio
Dice measurement
accuracy) and the quantitative indicators for diagnosis of glaucoma (i.e.
CDR error
average error
root mean square error) are summarized. Thanks to the continuous development of deep learning technology glaucoma diagnostic technology has become possible to obtain more precise segmentation through continuous training. Many image processing and machine learning techniques are widely used in the existing optic disc and optic cup segmentation methods. We demonstrate the diagnosis of glaucoma research algorithms to review the features and links between various algorithms. It is beneficial to promote the application of optic disc and optic cup segmentation in the clinical screening of glaucoma diseases further. Additionally
it can improve the work efficiency of clinicians
which provides an important theoretical research significance for the clinical diagnosis of glaucoma.
Agrawal D K, Kirar B S and Pachori R B. 2019. Automated glaucoma detection using quasi-bivariate variational mode decomposition from fundus images. IET Image Processing, 13(13): 2401-2408 [DOI: 10.1049/iet-ipr.2019.0036]
Akbar S, Akram M U, Sharif M, Tariq A and Yasin U U. 2018. Arteriovenous ratio and papilledema based hybrid decision support system for detection and grading of hypertensive retinopathy. Computer Methods and Programs in Biomedicine, 154: 123-141 [DOI: 10.1016/j.cmpb.2017.11.014]
Bajwa M N, Malik M I, Siddiqui S A, Dengel A, Shafait F, Neumeier W and Ahmed S. 2019. Two-stage framework for optic disc localization and glaucoma classification in retinal fundus images using deep learning. BMC Medical Informatics and Decision Making, 19(1): #136 [DOI: 10.1186/s12911-019-0842-8]
Balakrishnan U. 2017. NDC-IVM: an automatic segmentation of optic disc and cup region from medical images for glaucoma detection. Journal of Innovative Optical Health Sciences, 10(3): #1750007 [DOI: 10.1142/S1793545817500079]
Bechar M E, Settouti N, Barra V and Chikh M A. 2018. Semi-supervised superpixel classification for medical images segmentation: application to detection of glaucoma disease. Multidimensional Systems and Signal Processing, 29(3): 979-998 [DOI: 10.1007/s11045-017-0483-y]
Biswal B, Vyshnavi E, Sairam M V S and Rout P K. 2020. Robust retinal optic disc and optic cup segmentation via stationary wavelet transform and maximum vessel pixel sum. IET Image Processing, 14(4): 592-602 [DOI: 10.1049/iet-ipr.2019.0845]
Biswas S and Ghoshal D. 2016. Blood cell detection using thresholding estimation based watershed transformation with sobel filter in frequency domain. Procedia Computer Science, 89: 651-657 [DOI: 10.1016/j.procs.2016.06.029]
Bock R, Meier J, Nyúl L G, Hornegger J and Michelson G. 2010. Glaucoma risk index: automated glaucoma detection from color fundus images. Medical Image Analysis, 14(3): 471-481 [DOI: 10.1016/j.media.2009.12.006]
Bouacheria M, Cherfa Y, Cherfa A and Belkhamsa N. 2020. Automatic glaucoma screening using optic nerve head measurements and random forest classifier on fundus images. Physical and Engineering Sciences in Medicine, 43(4): 1265-1277 [DOI: 10.1007/s13246-020-00930-y]
Carbajal-Degante E, Olveres J and Escalante-Ramírez B. 2018. A multiphase active contour model based on the Hermite transform for texture segmentation//Proceedings Volume 10679, Optics, Photonics, and Digital Technologies for Imaging Applications V. Strasbourg, France: SPIE: #106791H [ DOI: 10.1117/12.2306541 http://dx.doi.org/10.1117/12.2306541 ]
Chalakkal R J, Abdulla W H and Thulaseedharan S S. 2018. Automatic detection and segmentation of optic disc and fovea in retinal images. IET Image Processing, 12(11): 2100-2110 [DOI: 10.1049/iet-ipr.2018.5666]
Cheng J, Liu J, Xu Y W, Yin F S, Wong D W K, Tan N M, Tao D C, Cheng C Y, Aung T and Wong T Y. 2013. Superpixel classification based optic disc and optic cup segmentation for glaucoma screening. IEEE Transactions on Medical Imaging, 32(6): 1019-1032 [DOI: 10.1109/TMI.2013.2247770]
Cheng J, Zhang Z, Tao D C, Wong D W K, Liu J, Baskaran M, Aung T and Wong T Y. 2017. Similarity regularized sparse group lasso for cup to disc ratio computation. Biomedical Optics Express, 8(8): 3763-3777 [DOI: 10.1364/BOE.8.003763]
Díaz-García J and Caro-Lopera F. 2017. Estimation of mean form and mean form difference under elliptical laws. Electronic Journal of Statistics, 11(1): 2424-2460 [DOI: 10.1214/17-EJS1289]
Dong J W and Yang H Y. 2008. An overview on active contour models. Information Technology and Informatization, (5): 44-47
董吉文, 杨海英. 2008. 活动轮廓模型综述. 信息技术与信息化, (5): 44-47[DOI: 10.3969/j.issn.1672-9528.2008.05.016]
Ejaz K, Rahim M S M, Bajwa U I, Chaudhry H, Rehman A and Ejaz F. 2021. Hybrid segmentation method with confidence region detection for tumor identification. IEEE Access, 9: 35256-35278 [DOI: 10.1109/ACCESS.2020.3016627]
Gui B, Shuai R J and Chen P. 2018. Optic disc localization algorithm based on improved corner detection. Procedia Computer Science, 131: 311-319 [DOI: 10.1016/j.procs.2018.04.169]
Haleem M S, Han L X, Hemert J V, Li B H, Fleming A, Pasquale L R and Song B J. 2018. A novel adaptive deformable model for automated optic disc and cup segmentation to aid glaucoma diagnosis. Journal of Medical Systems, 42(1): #20 [DOI: 10.1007/s10916-017-0859-4]
Heneghan C, Flynn J, O'Keefe M and Cahill M. 2002. Characterization of changes in blood vessel width and tortuosity in retinopathy of prematurity using image analysis. Medical Image Analysis, 6(4): 407-429 [DOI: 10.1016/S1361-8415(02)00058-0]
Imbert C. 2009. Level set approach for fractional mean curvature flows. Interfaces and Free Boundaries, 11(1): 153-176 [DOI: 10.4171/IFB/207]
Jiang Y M, Duan L X, Cheng J, Gu Z W, Xia H, Fu H Z, Li C S and Liu J. 2020. JointRCNN: a region-based convolutional neural network for optic disc and cup segmentation. IEEE Transactions on Biomedical Engineering, 67(2): 335-343 [DOI: 10.1109/TBME.2019.2913211]
Jimenez S, Gonzalez F A and Gelbukh A. 2016. Mathematical properties of soft cardinality: enhancing Jaccard, Dice and cosine similarity measures with element-wise distance. Information Sciences, 367-368: 373-389 [DOI: 10.1016/j.ins.2016.06.012]
Kaur J and Mittal D. 2018. A generalized method for the segmentation of exudates from pathological retinal fundus images. Biocybernetics and Biomedical Engineering, 38(1): 27-53 [DOI: 10.1016/j.bbe.2017.10.003]
Keerthiveena B, Esakkirajan S, Selvakumar K and Yogesh T. 2020. Computer-aided diagnosis of retinal diseases using multidomain feature fusion. International Journal of Imaging Systems and Technology, 30(2): 367-379 [DOI: 10.1002/ima.22379]
Khalid N E A, Noor N M and Ariff N M. 2014. Fuzzy c-means (FCM) for optic cup and disc segmentation with morphological operation. Procedia Computer Science, 42: 255-262 [DOI: 10.1016/j.procs.2014.11.060]
Khan M W, Sharif M, Yasmin M and Saba T. 2017. CDR based glaucoma detection using fundus images: a review. International Journal of Applied Pattern Recognition, 4(3): 261-306 [DOI: 10.1504/IJAPR.2017.086596]
Krizhevsky A, Sutskever I and Hinton G E. 2017. Imagenet classification with deep convolutional neural networks. Communications of the ACM, 60(6): 84-90 [DOI: 10.1145/3065386]
Lee J, Tang H and Park J. 2018. Energy efficient canny edge detector for advanced mobile vision applications. IEEE Transactions on Circuits and Systems for Video Technology, 28(4): 1037-1046 [DOI: 10.1109/TCSVT.2016.2640038]
Li G. 2018. Research on Diabetic Retinopathy Using Deep Learning Method. Shanghai: Shanghai Jiao Tong University
李郭. 2018. 基于深度学习的糖尿病视网膜眼底图像病变分析算法研究. 上海: 上海交通大学[ DOI: 10.27307/d.cnki.gsjtu.2018.002016 http://dx.doi.org/10.27307/d.cnki.gsjtu.2018.002016 ]
Li X X, Yin P P, Zhi Y and Duan C. 2020. Vertical lane line detection technology based on Hough transform. IOP Conference Series: Earth and Environmental Science, 440(3): #032126 [DOI: 10.1088/1755-1315/440/3/032126]
Ling F, Fang S M, Li W B, Li X D, Xiao F, Zhang Y H and Du Y. 2014. Post-processing of interpolation-based super-resolution mapping with morphological filtering and fraction refilling. International Journal of Remote Sensing, 35(13): 5251-5262 [DOI: 10.1080/2150704X.2014.938179]
Luo L, Liu S, Tong X Y, Jiang P R, Yuan C, Zhao X H and Shang F. 2019. Carotid artery segmentation using level set method with double adaptive threshold (DATLS) on TOF-MRA images. Magnetic Resonance Imaging, 63: 123-130 [DOI: 10.1016/j.mri.2019.08.002]
Mitra A, Banerjee P S, Roy S, Roy S and Setua S K. 2018. The region of interest localization for glaucoma analysis from retinal fundus image using deep learning. Computer Methods and Programs in Biomedicine, 165: 25-35 [DOI: 10.1016/j.cmpb.2018.08.003]
Mittapalli P S and Kande G B. 2016. Segmentation of optic disk and optic cup from digital fundus images for the assessment of glaucoma. Biomedical Signal Processing and Control, 24: 34-46 [DOI: 10.1016/j.bspc.2015.09.003]
Moon J, Park K H, Kim D M and Kim S H. 2018. Factors affecting ISNT rule satisfaction in normal and glaucomatous eyes. Korean Journal of Ophthalmology, 32(1): 38-44 [DOI: 10.3341/kjo.2017.0031]
Morales S, Naranjo V, Angulo J and Alcañiz M. 2013. Automatic detection of optic disc based on PCA and mathematical morphology. IEEE Transactions on Medical Imaging, 32(4): 786-796 [DOI: 10.1109/TMI.2013.2238244]
Mukherjee R, Kundu S, Dutta K, Sen A and Majumdar S. 2019. Erratum to: predictive diagnosis of glaucoma based on analysis of focal notching along the neuro-retinal rim using machine learning. Pattern Recognition and Image Analysis, 29(4): 775-775 [DOI: 10.1134/S1054661819040230]
Niu J, Li Y B and Song R. 2005. An overview of active contour models. Journal of Hangzhou Dianzi University, 25(3): 67-70
牛君, 李贻斌, 宋锐. 2005. 活动轮廓模型综述. 杭州电子科技大学学报, 25(3): 67-70[DOI: 10.13954/j.cnki.hdu.2005.03.019]
Oh W O, Shim K W, Yeom I, Park I T and Heo Y. 2021. Features and diversity of symptoms of moyamoya disease in adolescents: a cluster analysis. Journal of Advanced Nursing, Journal of Advanced Nursing, 77(5): 2319-2327 [DOI: 10.1111/jan.14723]
Osher S and Sethian J A. 1988. Fronts propagating with curvature-dependent speed: algorithms based on Hamilton-Jacobi formulations. Journal of Computational Physics, 79(1): 12-49 [DOI: 10.1016/0021-9991(88)90002-2]
Polomčić D, Gligorić Z, Bajić Dand CvijovićCˇ. 2017. A hybrid model for forecasting groundwater levels based on fuzzy C-mean clustering and singular spectrum analysis. Water, 9(7): #541 [DOI: 10.3390/w9070541]
Qin Y S. 2019. Intelligent Diagnosis of Glaucoma Based on Deep Learning. Hefei: University of Science and Technology of China
秦运输. 2019. 基于深度学习的青光眼智能诊断研究. 合肥: 中国科学技术大学[ DOI: 10.27517/d.cnki.gzkju.2019.000304 http://dx.doi.org/10.27517/d.cnki.gzkju.2019.000304 ]
Rodrigues L C and Marengoni M. 2017. Segmentation of optic disc and blood vessels in retinal images using wavelets, mathematical morphology and Hessian-based multi-scale filtering. Biomedical Signal Processing and Control, 36: 39-49 [DOI: 10.1016/j.bspc.2017.03.014]
Sandra M M, Valeriana N O, Jesus A L, and Mariano Luis A R. 2013. Automatic detection of optic disc based on PCA and mathematical morphology. IEEE Transactions on Medical Imaging, 32(4): 786-796 [DOI:10.1109/TMI.2013.2238244]
Sarkar P, Kumar A, Singh K H, Ghosh R and Singh T N. 2018. Pore system, microstructure and porosity characterization of Gondwana shale of Eastern India using laboratory experiment and watershed image segmentation algorithm. Marine and Petroleum Geology, 94: 246-260 [DOI: 10.1016/j.marpetgeo.2018.04.006]
Shahid M and Taj I A. 2018. Retracted: robust retinal vessel segmentation using vessel's location map and frangi enhancement filter. IET Image Processing, 12(4): 494-501 [DOI: 10.1049/iet-ipr.2017.0457]
Sivaswamy J, Krishnadas S R, Joshi G D, Jain M and Tabish A U S. 2014. Drishti-GS: retinal image dataset for optic nerve head(ONH) segmentation//2014 IEEE 11th International Symposium on Biomedical Imaging (ISBI). Beijing, China: IEEE: 53-56 [ DOI: 10.1109/ISBI.2014.6867807 http://dx.doi.org/10.1109/ISBI.2014.6867807 ]
Sun X, Xu Y W, Zhao W, You T Y and Liu J. 2018. Optic disc segmentation from retinal fundus images via deep object detection networks//Proceedings of the 40th AnnualInternational Conference of the IEEE Engineering in Medicine and Biology Society. Honolulu, USA: IEEE: 5954-5957 [ DOI: 10.1109/EMBC.2018.8513592 http://dx.doi.org/10.1109/EMBC.2018.8513592 ]
Tang W J, Zhu J M and Zhang H. 2017. Segmentation of medical images based on bias and double level set algorithm. Radio Communications Technology, 43(4): 30-34
唐文杰, 朱家明, 张辉. 2017. 基于偏移场的双水平集医学图像分割算法. 无线电通信技术, 43(4): 30-34[DOI: 10.3969/j.issn.1003-3114.2017.04.08]
Thakur N and Juneja M. 2018. Survey on segmentation and classification approaches of optic cup and optic disc for diagnosis of glaucoma. Biomedical Signal Processing and Control, 42: 162-189 [DOI: 10.1016/j.bspc.2018.01.014]
Thakur N and Juneja M. 2019. Optic disc and optic cup segmentation from retinal images using hybrid approach. Expert Systems with Applications, 127: 308-322 [DOI: 10.1016/j.eswa.2019.03.009]
Tian Z Q, Zheng Y Y, Li X J, Du S Y and Xu X Y. 2020. Graph convolutional network based optic disc and cup segmentation on fundus images. Biomedical Optics Express, 11(6): 3043-3057 [DOI: 10.1364/BOE.390056]
Veena H N, Muruganandham A and Kumaran T S. 2020. A review on the optic disc and optic cup segmentation and classification approaches over retinal fundus images for detection of glaucoma. SN Applied Sciences, 2(9): #1476 [DOI: 10.1007/s42452-020-03221-z]
Wang X H and Fang L L. 2013. Survey of image segmentation based on active contour model. Pattern Recognition and Artificial Intelligence, 26(8): 751-760
王相海, 方玲玲. 2013. 活动轮廓模型的图像分割方法综述. 模式识别与人工智能, 26(8): 751-760[DOI:10.16451/j.cnki.issn1003-6059.2013.08.005]
Wang Y, Yu X S, Chi J N, Lei X L and Wu C D. 2020. Segmentation of optic cup and disc based on two-layer level set describer in retinal fundus images. Journal of Image and Graphics, 25(6): 1260-1270
王莹, 于晓升, 迟剑宁, 雷晓亮, 吴成东. 2020. 双层水平集描述眼底图像视杯视盘分割. 中国图象图形学报, 25(6): 1260-1270[DOI: 10.1155/2019/4836296]
Wong D W K, Liu J, Lim J H, Jia X, Yin F, Li H and Wong T Y. 2008. Level-set based automatic cup-to-disc ratio determination using retinal fundus images in ARGALI//Proceedings of the 30th Annual International Conference of the IEEE Engineering in Medicine and Biology Society. Vancouver, Canada: IEEE: 2266-2269 [ DOI: 10.1109/IEMBS.2008.4649648 http://dx.doi.org/10.1109/IEMBS.2008.4649648 ]
Yuan X, ZhengX J, Ji B, Li M and Li B. 2020. Joint optic disc and cup segmentation based on residual multi-scale fully convolutional neural network. Journal of Biomedical Engineering, 37(5): 875-884
袁鑫, 郑秀娟, 吉彬, 李淼, 李彬. 2020. 基于多尺度残差卷积神经网络的视杯视盘联合分割. 生物医学工程学杂志, 37(5): 875-884[DOI: 10.7507/1001-5515.201909006]
Zeng W, Fang X, Lin Y, Huang X and Yao Y. 2019. On the errors-in-variables model with inequality constraints of dependent variables for geodetic transformation. Survey Review, 51(365): 166-171 [DOI: 10.1080/00396265.2017.1396407]
Zhang J, Zheng D C, Wu K and Zhang X Q. 2019. The optimum conditions for preparing briquette made from millet bran using Generalized Distance Function. Renewable Energy, 140: 692-703 [DOI: 10.1016/j.renene.2019.03.079]
Zhang L, Zhang J H, Li Z G and Song Y C. 2020. A multiple-channel and atrous convolution network for ultrasound image segmentation. Medical Physics, 47(12): 6270-6285 [DOI: 10.1002/mp.14512]
Zhang T, Tian Y, Wang Z and Wang S D. 2020. Adaptive threshold image segmentation based on definition evaluation. Journal of Northeastern University (Natural Science), 41(9): 1231-1238
张田, 田勇, 王子, 王昭东. 2020. 基于清晰度评价的自适应阈值图像分割法. 东北大学学报(自然科学版), 41(9): 1231-1238[DOI:10.12068/j.issn.1005-3026.2020.09.003]
Zhang Z, Liu J, Cherian N S, Sun Y, Lim J H, Wong W K, Tan N M, Lu S J, Li H Q and Wong T Y. 2009. Convex hull based neuro-retinal optic cup ellipse optimization in glaucoma diagnosis//Proceedings of the 2009 Annual International Conference of the IEEE Engineering in Medicine and Biology Society. Minneapolis, USA: IEEE: 1441-1444 [ DOI: 10.1109/IEMBS.2009.5332913 http://dx.doi.org/10.1109/IEMBS.2009.5332913 ]
Zhang Z, Yin F S, Liu J, Wong W K, Tan N M, Lee B H, Cheng J and Wong T Y. 2010. ORIGA-light: an online retinal fundus image database for glaucoma analysis and research//Proceedings of 2010 Annual International Conference of the IEEE Engineering in Medicine and Biology. Buenos Aires, Argentina: IEEE: 3065-3068 [ DOI: 10.1109/IEMBS.2010.5626137 http://dx.doi.org/10.1109/IEMBS.2010.5626137 ]
Zhao X Y and Chen M H. 2018. A survey of image segmentation based on clustering. Information Technology, (6): 92-94, 99
赵祥宇, 陈沫涵. 2018. 基于聚类的图像分割方法综述. 信息技术, (6): 92-94, 99[DOI: 10.13274/j.cnki.hdzj.2018.06.019]
Zheng S, Fan H J, Tang Y D and Wang Y. 2014. Automatic segmentation of optic disc and cup using multiphase active contour model in fundus images. Journal of Image and Graphics, 19(11): 1604-1612
郑姗, 范慧杰, 唐延东, 王琰. 2014. 多相主动轮廓模型的眼底图像杯盘分割. 中国图象图形学报, 19(11): 1604-1612[DOI: 10.11834/jig.20141108]
Zhou W, Yi Y G, Gao Y and Dai J Y. 2019. Optic disc and cup segmentation in retinal images for glaucoma diagnosis by locally statistical active contour model with structure prior. Computational and Mathematical Methods in Medicine, 2019: #8973287 [DOI: 10.1155/2019/8973287]
Zilly J, Buhmann J M and MahapatraD. 2017. Glaucoma detection using entropy sampling and ensemble learning for automatic optic cup and disc segmentation. Computerized Medical Imaging and Graphics, 55: 28-41 [DOI: 10.1016/j.compmedimag.2016.07.012]
相关文章
相关作者
相关机构
京公网安备11010802024621