Review of Various Tasks Performed in the Preprocessing Phase of a Diabetic Retinopathy Diagnosis System

Author(s): Muhammad Nadeem Ashraf, Muhammad Hussain, Zulfiqar Habib*

Journal Name: Current Medical Imaging
Formerly: Current Medical Imaging Reviews

Volume 16 , Issue 4 , 2020

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Graphical Abstract:


Diabetic Retinopathy (DR) is a major cause of blindness in diabetic patients. The increasing population of diabetic patients and difficulty to diagnose it at an early stage are limiting the screening capabilities of manual diagnosis by ophthalmologists. Color fundus images are widely used to detect DR lesions due to their comfortable, cost-effective and non-invasive acquisition procedure. Computer Aided Diagnosis (CAD) of DR based on these images can assist ophthalmologists and help in saving many sight years of diabetic patients. In a CAD system, preprocessing is a crucial phase, which significantly affects its performance. Commonly used preprocessing operations are the enhancement of poor contrast, balancing the illumination imbalance due to the spherical shape of a retina, noise reduction, image resizing to support multi-resolution, color normalization, extraction of a field of view (FOV), etc. Also, the presence of blood vessels and optic discs makes the lesion detection more challenging because these two artifacts exhibit specific attributes, which are similar to those of DR lesions. Preprocessing operations can be broadly divided into three categories: 1) fixing the native defects, 2) segmentation of blood vessels, and 3) localization and segmentation of optic discs. This paper presents a review of the state-of-the-art preprocessing techniques related to three categories of operations, highlighting their significant aspects and limitations. The survey is concluded with the most effective preprocessing methods, which have been shown to improve the accuracy and efficiency of the CAD systems.

Keywords: Computer aided diagnose, diabetic retinopathy, fundus image preprocessing, blood vessels, optic discs, convolutional neural networks.

Cho NH, Shaw JE, Karuranga S, et al. IDF Diabetes Atlas: Global estimates of diabetes prevalence for 2017 and projections for 2045. Diabetes Res Clin Pract 2018; 138: 271-81.
[] [PMID: 29496507]
Zhang Z, Srivastava R, Liu H, et al. A survey on computer aided diagnosis for ocular diseases. BMC Med Inform Decis Mak 2014; 14(1): 80.
[] [PMID: 25175552]
Ashraf MN, Habib Z, Hussain M. Computer aided diagnosis of Diabetic Retinopathy. LAP LAMBERT Academic Publishing 2015.
Tufail A, Rudisill C, Egan C, et al. Automated diabetic retinopathy image assessment software: Diagnostic accuracy and cost-effectiveness compared with human graders. Ophthalmology 2017; 124(3): 343-51.
[] [PMID: 28024825]
Kauppi T, Kalesnykiene V, Kamarainen J-K, et al. DIARETDB1 diabetic retinopathy database and evaluation protocol. In: Proceedings of the British Machine Conference. 2007 Jul 17-18; Aberystwyth, Wales: BMVA Press pp. 1-10.
Acharya UR, Chua CK, Ng EYK, Yu W, Chee C. Application of higher order spectra for the identification of DR stages. J Med Syst 2008; 32(6): 481-8.
[] [PMID: 19058652]
Aquino A, Gegúndez-Arias ME, Marin D. Detecting the optic disc boundary in digital fundus images using morphological, edge detection, and feature extraction techniques. IEEE Trans Med Imaging 2010; 29(11): 1860-9.
[] [PMID: 20562037]
Ashraf MN, Habib Z, Hussain M. Texture feature analysis of digital fundus images for early detection of diabetic retinopathy. In: Banissi E, Sarfraz M, Eds. 11th International Conference on Computer Graphics, Imaging and Visualization: New Techniques and Trends, CGIV; 2014 August 6-8; Singapore; IEEE Computer Society Press; USA. 2014; pp. 57-62.
Askew DA, Crossland L, Ware RS, et al. Diabetic retinopathy screening and monitoring of early stage disease in general practice: design and methods. Contemp Clin Trials 2012; 33(5): 969-75.
[] [PMID: 22575797]
Autio I. Borra´ s JC, Immonen I, Jalli P, Ukkonen E. A voting margin approach for the detection of retinal micro-aneurysms. In Proceedings of the Fifth IASTED International Conference on Visualization, imagine, and Image Processing. 2005 Sep 7-9; Benidorm, Spain: ACTA Press pp. 511-7.
Bae JP, Kim KG, Kang HC, Jeong CB, Park KH, Hwang JM. A study on hemorrhage detection using hybrid method in fundus images. J Digit Imaging 2011; 24(3): 394-404.
[] [PMID: 20177733]
Baudoin CE, Laÿ BJ, Klein JC. Automatic detection of microaneurysms in diabetic fluorescein angiography. Rev Epidemiol Sante Publique 1984; 32(3-4): 254-61.
[PMID: 6522738]
Bhalerao A, Patanaik A, Anand S, Saravanan P. Robust detection of microaneurysms for sight threatening retinopathy screening. In: Sixth Indian Conference on Computer Vision, Graphics & Image Processing. 2008 Dec 16-19; Bhubaneswar, India IEEE 2009; pp.. 520-27.
Dupas B, Walter T, Erginay A, et al. Evaluation of automated fundus photograph analysis algorithms for detecting microaneurysms, haemorrhages and exudates, and of a computer-assisted diagnostic system for grading diabetic retinopathy. Diabetes Metab 2010; 36(3): 213-20.
[] [PMID: 20219404]
Ege BM, Hejlesen OK, Larsen OV, et al. Screening for diabetic retinopathy using computer based image analysis and statistical classification. Comput Methods Programs Biomed 2000; 62(3): 165-75.
[] [PMID: 10837904]
Fathi A, Naghsh-Nilchi AR. Integrating adaptive neuro-fuzzy inference system and local binary pattern operator for robust retinal blood vessels segmentation. Neural Comput Appl 2013; 22: 163-74.
Fleming AD, Goatman KA, Williams GJP, Philip S, Sharp PF, Olson JA. Automated detection of blot haemorrhages as a sign of referable diabetic retinopathy. In: Proceedings of 12th the Medical Image Understanding and Analysis. 2008 Jul 2-3; Dundee, UK: IEEE pp 235-39.
Fleming AD, Philip S, Goatman KA, Olson JA, Sharp PF. Automated microaneurysm detection using local contrast normalization and local vessel detection. IEEE Trans Med Imaging 2006; 25(9): 1223-32.
[] [PMID: 16967807]
Frame AJ, Undrill PE, Cree MJ, et al. A comparison of computer based classification methods applied to the detection of microaneurysms in ophthalmic fluorescein angiograms. Comput Biol Med 1998; 28(3): 225-38.
[] [PMID: 9784961]
Garc’ıa M, S’anchez CI, L’opez MI, D’ıez A, Hornero’ R. Automatic detection of red lesions in retinal images using a multilayer perceptron neural network. In: Proceedings of 30th Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBS2008). 2008 Aug 20-24; Vancouver BC, Canada: IEEE pp. 25-8.
Gardner G, Keating D, Williamson T, Elliot A. Detection of diabetic retinopathy using neural network analysis of fundus images. Br J Ophthalmol 1996; 80(11): 937-48.
[] [PMID: 8976716]
Giancardo L. Quality analysis of retina images for the automatic diagnosis of diabetic retinopathy. MSc Thesis, Université de Bourgogne, France 2008.
Grisan E, Ruggeri A. Segmentation of candidate dark lesions in fundus images based on local thresholding and pixel density. In: Proceedings of 29th Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBS2007). 2007 Aug 22-26; Lyon, France: IEEE ; pp. 6735-8.
Tang L, Niemeijer M, Reinhardt JM, Garvin MK, Abràmoff MD. Splat feature classification with application to retinal hemorrhage detection in fundus images. IEEE Trans Med Imaging 2013; 32(2): 364-75.
[] [PMID: 23193310]
Hipwell JH, Strachan F, Olson JA, McHardy KC, Sharp PF, Forrester JV. Automated detection of microaneurysms in digital red-free photographs: a diabetic retinopathy screening tool. Diabet Med 2000; 17(8): 588-94.
[] [PMID: 11073180]
Hoover A, Goldbaum M. Locating the optic nerve in a retinal image using the fuzzy convergence of the blood vessels. IEEE Trans Med Imaging 2003; 22(8): 951-8.
[] [PMID: 12906249]
Köse C, Sevik U, Ikibaş C, Erdöl H. Simple methods for segmentation and measurement of diabetic retinopathy lesions in retinal fundus images. Comput Methods Programs Biomed 2012; 107(2): 274-93.
[] [PMID: 21757250]
Laÿ B. Analyse automatique des images angio fluorographiques au cours de la retinopathie diabetique PhD Dissertation Centre of Mathematical Morphology Paris, France 1983.
Youssif AR, Ghalwash AZ, Ghoneim AR. Optic disc detection from normalized digital fundus images by means of a vessels’ direction matched filter. IEEE Trans Med Imaging 2008; 27(1): 11-8.
[] [PMID: 18270057]
Mendonc a A, Campilho AJ, Nunes JM. Automatic segmentation of microaneurysms in retinal angiograms of diabetic patients. In: Procedings of IEEE International Conference on Image Analysis and Processing (ICIAP’ 99); 1999 Sept 27-29; Venice, Italy. IEEE; pp. 728-33.
Kanski JJ, Bowling B. Clinical ophthalmology: A systematic approach. 7th ed. Butterworth: Heinemann Elsevier 2011.
Mahesh KK. A survey of automated techniques for retinal disease identification in diabetic retinopathy. IJOART 2013; 2(5): 199-216.
Abràmoff MD, Garvin MK, Sonka M. Retinal imaging and image analysis. IEEE Rev Biomed Eng 2010; 3: 169-208.
[] [PMID: 22275207]
Jitpakdee P, Aimmanee P, Uyyanonvara BS. A survey on hemorrhage detection in diabetic retinopathy retinal images. In: Proceedings of 9th International Conference on Electrical Engineering/Electronics, Computer, Telecommunications and Information Technology (ECTI-CON). 2012 May 16-18; Phetchaburi, Bangkok, Thailand: IEEE pp. 1-4.
Mookiah MRK, Acharya UR, Chua CK, Lim CM, Ng EYK, Laude A. Computer-aided diagnosis of diabetic retinopathy: A review. Comput Biol Med 2013; 43(12): 2136-55.
[] [PMID: 24290931]
Mapayi T, Viriri S, Tapamo JR. Comparative study of retinal vessel segmentation based on global thresholding techniques. Comput Math Methods Med 2015; 2015895267
[] [PMID: 25793012]
Besenczi R, Tóth J, Hajdu A. A review on automatic analysis techniques for color fundus photographs. Comput Struct Biotechnol J 2016; 14: 371-84.
[] [PMID: 27800125]
Amin J, Sharif M, Yasmin M. A review on recent developments for detection of diabetic retinopathy. Scientifica (Cairo) 2016; 20166838976
[] [PMID: 27777811]
Qureshi I, Sharif M, Yasmin M, Raza M, Javed MY. Computer aided systems for diabetic retinopathy detection using digital fundus images: A survey. Curr Med Imaging 2016; 12(4): 234-41.
Mansour RF. Evolutionary computing enriched computer-aided diagnosis system for diabetic retinopathy: A survey. IEEE Rev Biomed Eng 2017; 10: 334-49.
[] [PMID: 28534786]
Almotiri J, Elleithy K, Elleithy A. Retinal vessels segmentation techniques and algorithms: A survey. Appl Sci (Basel) 2018; 8(2): 155.
Ghanchi F. The Royal College of Ophthalmologists’ clinical guidelines for diabetic retinopathy: a summary. Eye (Lond) 2013; 27(2): 285-7.
[] [PMID: 23306724]
Shotliff KP, Duncan G. Diabetic retinopathy screening programmes and reducing ophthalmologists’ workload. Diabet Med 2006; 23(4): 449-50.
[] [PMID: 16620278]
Safi H, Safi S, Hafezi-Moghadam A, Ahmadieh H. Early detection of diabetic retinopathy. Surv Ophthalmol 2018; 63(5): 601-8.
[] [PMID: 29679616]
Augustin A, Bandello F, Coscas G, et al. Macular edema a practical approach. Basel: Karger Publishers 2010.
Sharp PF, Olson J, Strachan F, et al. The value of digital imaging in diabetic retinopathy. Health Technol Assess 2003; 7(30): 1-119.
[] [PMID: 14604499]
Tariq A, Akram MU, Shaukat A, Khan SA. Automated detection and grading of diabetic maculopathy in digital retinal images. J Digit Imaging 2013; 26(4): 803-12.
[] [PMID: 23325123]
Medhi JP, Dandapat S. An effective fovea detection and automatic assessment of diabetic maculopathy in color fundus images. Comput Biol Med 2016; 74(74): 30-44.
[] [PMID: 27174686]
Usman Akram M, Khalid S, Tariq A, Khan SA, Azam F. Detection and classification of retinal lesions for grading of diabetic retinopathy. Comput Biol Med 2014; 45: 161-71.
[] [PMID: 24480176]
Zhao YQ, Wang XH, Wang XFY, Shih F. Retinal vessels segmentation based on level set and region growing. Pattern Recognit 2014; 47(7): 2437-46.
Woźniak T, Strzelecki M, Majos A, Stefańczyk L. 3D vascular tree segmentation using a multiscale vesselness function and a level set approach. Biocybern Biomed Eng 2017; 37(1): 66-77.
Walter T, Massin P, Erginay A, Ordonez R, Jeulin C, Klein JC. Automatic detection of microaneurysms in color fundus images. Med Image Anal 2007; 11(6): 555-66.
[] [PMID: 17950655]
Fraz MM, Remagnino P, Hoppe A, et al. Blood vessel segmentation methodologies in retinal images--a survey. Comput Methods Programs Biomed 2012; 108(1): 407-33.
[] [PMID: 22525589]
Kar SS, Maity SP. Automatic detection of retinal lesions for screening of diabetic retinopathy. IEEE Trans Biomed Eng 2018; 65(3): 608-18.
[] [PMID: 28541892]
Jiang Z, Yepez J, An S, Ko S. Fast, accurate and robust retinal vessel segmentation system. Biocybern Biomed Eng 2017; 37(3): 412-21.
Guo Y, Budak Ü, Şengür A, Smarandache F. A retinal vessel detection approach based on shearlet transform and indeterminacy filtering on fundus images. Symmetry (Basel) 2017; 9(10): 235-45.
Zhu C, Zou B, Zhao R, et al. Retinal vessel segmentation in colour fundus images using Extreme Learning Machine. Comput Med Imaging Graph 2017; 55: 68-77.
[] [PMID: 27289537]
Kar SS, Maity SP. Blood vessel extraction and optic disc removal using curvelet transform and kernel fuzzy c-means. Comput Biol Med 2016; 70: 174-89.
[] [PMID: 26848729]
Fan Z, Rong Y, Lu J, et al. Automated blood vessel segmentation in fundus image based on Integral channel features and random forests. In: Proceedings of 12th World Congress on Intelligent Control and Automation (WCICA). 2016 Jun 12-16; Guilin, China: IEEE pp. pp. 2063-8.
Ali Shah SA, Laude A, Faye I, Tang TB. Automated microaneurysm detection in diabetic retinopathy using curvelet transform. J Biomed Opt 2016; 21(10) 101404
[] [PMID: 26868326]
Li Q, Feng B, Xie L, Liang P, Zhang H, Wang T. A cross-modality learning approach for vessel segmentation in retinal images. IEEE Trans Med Imaging 2016; 35(1): 109-18.
[] [PMID: 26208306]
Aslani S, Sarnel H. A new supervised retinal vessel segmentation method based on robust hybrid features. Biomed Signal Process Control 2016; 30: 1-12.
Wang S, Yin Y, Cao G, Wei B, Zheng Y, Yang G. Hierarchical retinal blood vessel segmentation based on feature and ensemble learning. Neurocomput 2015; 149(2): 708-17.
Waheed A, Waheed Z, Akram MU, Shaukat A. Removal of false blood vessels using Shape based features and Image Inpainting. J Sens 2015; 2015 839894
Imani E, Javidi M, Pourreza HR. Improvement of retinal blood vessel detection using morphological component analysis. Comput Methods Programs Biomed 2015; 118(3): 263-79.
[] [PMID: 25697986]
Imani E, Pourreza HR, Banaee T. Fully automated diabetic retinopathy screening using morphological component analysis. Comput Med Imaging Graph 2015; 43: 78-88.
[] [PMID: 25863517]
Zhao Y, Rada L, Chen K, Harding SP, Zheng Y. Automated vessel segmentation using infinite perimeter active contour model with hybrid region information with application to retinal images. IEEE Trans Med Imaging 2015; 34(9): 1797-807.
[] [PMID: 25769147]
Fraz MM, Welikala RA, Rudnicka AR, Owen CG, Strachan DP, Barman SA. QUARTZ: Quantitative Analysis of Retinal Vessel Topology and Size – an automated system for quantification of retinal vessels morphology. Expert Syst Appl 2015; 42: 7221-34.
Roychowdhury S, Koozekanani DD, Parhi KK. Blood vessel segmentation of fundus images by major vessel extraction and subimage classification. IEEE J Biomed Health Inform 2015; 19(3): 1118-28.
[PMID: 25014980]
Mane VM, Kawadiwale RB, Jadhav DV. Detection of red lesions in diabetic retinopathy affected fundus images. In: Proceedings of IEEE International Advance Computing Conference (IACC). 2015 Jun 12-13; Banglore, India.: IEEE; pp. 56-60.
Mapayi T, Viriri S, Tapamo JR. Adaptive thresholding technique for retinal vessel segmentation based on GLCM-energy information. Comput Math Methods Med 2015; 2015 597475
[] [PMID: 25802550]
Gross S, Klein M, Schneider D. Segmentation of blood vessel structures in retinal fundus images with Logarithmic Gabor filters. Curr Med Imaging 2013; 9(2): 138-44.
Akram MU, Khan SA. Multilayered thresholding based blood vessel segmentation for screening of diabetic retinopathy. Eng Comput 2013; 29(2): 165-73.
Lazar I, Hajdu A. Retinal microaneurysm detection through local rotating cross-section profile analysis. IEEE Trans Med Imaging 2013; 32(2): 400-7.
[] [PMID: 23192523]
Salazar-Gonzalez A, Kaba D, Li Y, Liu X. Segmentation of the blood vessels and optic disk in retinal images. IEEE J Biomed Health Inform 2014; 18(6): 1874-86.
[] [PMID: 25265617]
Neto LC, Ramalho GLB, Neto JFSR, Veras RMS, Medeiros NSF. An unsupervised coarse-to-fine algorithm for blood vessel segmentation in fundus images. Expert Syst Appl 2017; 78: 182-92.
Mookiah MRK, Acharya UR, Martis RJ, Chua CK, Lim CM, Laude A. Evolutionary algorithm based classifier parameter tuning for automatic diabetic retinopathy grading: A hybrid feature extraction approach. Knowl Base Syst 2013; 39: 9-22.
Seo JW, Kim SD. Novel PCA-based color-to-gray image conversion. In: Proceedings of 20th IEEE International Conference on Image Processing (ICIP). 2013 Sep 15-18; Melbourne, Victoria, Australia: IEEE pp. 2279-83.
Singh NP, Srivastava R. Retinal blood vessels segmentation by using Gumbel probability distribution function based matched filter. Comput Methods Programs Biomed 2016; 129: 40-50.
[] [PMID: 27084319]
Seoud L, Hurtut T, Chelbi J, Cheriet F, Langlois JM. Red lesion detection using dynamic shape features for diabetic retinopathy screening. IEEE Trans Med Imaging 2016; 35(4): 1116-26.
[] [PMID: 26701180]
Liskowski P, Krawiec K. Segmenting retinal blood vessels with deep neural networks. IEEE Trans Med Imaging 2016; 35(11): 2369-80.
[] [PMID: 27046869]
Rodrigues LC, Marengoni M. Segmentation of optic disc and blood vessels in retinal images using wavelets, mathematical morphology and Hessian-based multi-scale filtering. Biomed Signal Process Control 2017; 36: 39-49.
Tan JH, Acharya UR, Bhandary SV, Chua KC, Sivaprasa S. Segmentation of optic disc, fovea and retinal vasculature using a single convolutional neural network. J Comput Sci 2017; 20: 70-9.
Azzopardi G, Strisciuglio N, Vento M, Petkov N. Trainable COSFIRE filters for vessel delineation with application to retinal images. Med Image Anal 2015; 19(1): 46-57.
[] [PMID: 25240643]
Balasubramanian L. Retinal blood vessel segmentation employing image processing and data mining techniques for computerized retinal image analysis. Biocybern Biomed Eng 2016; 36(1): 102-18.
Nagarajan P, Vinsley SS. Accurate optic disc boundary in digital fundus images using discrete shearlet transform and convex hull border estimator. J Med Imaging Health Inform 2016; 6(4): 978-83.
Abdullah M, Fraz MM, Barman SA. Localization and segmentation of optic disc in retinal images using circular Hough transform PeerJ 2016; 4(1): 1-23.
Marin D, Gegundez-Arias ME, Suero A, Bravo JM. Obtaining optic disc center and pixel region by automatic thresholding methods on morphologically processed fundus images. Comput Methods Programs Biomed 2015; 118(2): 173-85.
[] [PMID: 25433912]
Bharkad S. Automatic segmentation of optic disk in retinal images. Biomed Signal Process Control 2017; 31: 483-98.
Jiang Z, Zhang H, Wang Y, Ko SB. Retinal blood vessel segmentation using fully convolutional network with transfer learning. Comput Med Imaging Graph 2018; 68: 1-15.
[] [PMID: 29775951]
Hashim F, Salem NM, Seddik AF. Optic disc boundary detection from digital fundus images. J Med Imaging Health Inform 2015; 7: 50-6.
Dashtbozorg B, Mendonça AM, Campilho A. Optic disc segmentation using the sliding band filter. Comput Biol Med 2015; 56: 1-12.
[] [PMID: 25464343]
Mahendran G, Dhanasekaran R. Investigation of the severity level of diabetic retinopathy using supervised classifier algorithms. Comput Electr Eng 2015; 45(C): 312-23.
Zhang X, Thibault G, Decencière E, et al. Exudate detection in color retinal images for mass screening of diabetic retinopathy. Med Image Anal 2014; 18(7): 1026-43.
[] [PMID: 24972380]
Quellec G, Charrière K, Boudi Y, Cochener B, Lamard M. Deep image mining for diabetic retinopathy screening. Med Image Anal 2017; 39: 178-93.
[] [PMID: 28511066]
Staal J, Abràmoff MD, Niemeijer M, Viergever MA, van Ginneken B. Ridge-based vessel segmentation in color images of the retina. IEEE Trans Med Imaging 2004; 23(4): 501-9.
[] [PMID: 15084075]
Gagnon L, Lalonde M, Beaulieu M, Boucher MC. Procedure to detect anatomical structures in optical fundus images. Proc SPIE Int Soc Opt Eng. 2001; 4322(3):1218-25.
Kaba D, Wang C, Li Y, Salazar-Gonzalez A, Liu X, Serag A. Retinal blood vessels extraction using probabilistic modelling. Health Inf Sci Syst 2014; 2(1): 2.
[] [PMID: 25825666]
Tustison NJ, Avants BB, Cook PA, et al. N4ITK: improved N3 bias correction. IEEE Trans Med Imaging 2010; 29(6): 1310-20.
[] [PMID: 20378467]
Shah GA, Khan A, Shah AA, Raza M, Sharif M. A review on image contrast enhancement techniques using histogram equalization. Sci Int 2015; 27(2): 1297-302.
Läthéna G, Jonassonb J, Borga M. Blood vessel segmentation using multi-scale quadrature filtering. Pattern Recognit Lett 2010; 31(8): 762-7.
Xiangqian Wu , Baisheng Dai , Wei Bu . Optic disc localization using directional models. IEEE Trans Image Process 2016; 25(9): 4433-42.
[] [PMID: 27416600]
Sidibé D, Sadek I, Mériaudeau F. Discrimination of retinal images containing bright lesions using sparse coded features and SVM. Comput Biol Med 2015; 62: 175-84.
[] [PMID: 25935125]
Lee SS, Rajeswari M, Ramachandram D, Shaharuddin B. Screening of diabetic retinopathy - Automatic segmentation of optic disc in colour fundus images. In: Proceedings of 2nd International Conference on Distributed Frameworks for Multimedia Applications (IMB2006). 2006 Feb 13-16; Sydney, Australia: IEEE pp. 1-7.
Xiong L, Li H. An approach to locate optic disc in retinal images with pathological changes. Comput Med Imaging Graph 2016; 47: 40-50.
[] [PMID: 26650403]
Sato Y, Nakajima S, Shiraga N, et al. Three-dimensional multi-scale line filter for segmentation and visualization of curvilinear structures in medical images. Med Image Anal 1998; 2(2): 143-68.
[] [PMID: 10646760]
Frangi AF, Niessen WJ, Vincken KL, Viergever MA. Multiscale vessel enhancement filtering. Berlin: Springer 1998.
Orkisz MM, Bresson C, Magnin IE, Champin O, Douek PC. Improved vessel visualization in MR angiography by nonlinear anisotropic filtering. Magn Reson Med 1997; 37(6): 914-9.
[] [PMID: 9178244]
Vostatek P, Claridge E, Uusitalo H, Hauta-Kasari M, Fält P, Lensu L. Performance comparison of publicly available retinal blood vessel segmentation methods. Comput Med Imaging Graph 2017; 55: 2-12.
[] [PMID: 27515743]
Fritzsche K, Can A, Shen H, et al. Automated model based segmentation, tracing and analysis of retinal vasculature from digital fundus images State-of-The-Art Angiography, Applications and Plaque Imaging Using MR, CT, Ultrasound and X-rays. 1st ed. Boca Raton, FL, USA: CRC Press 2003; pp. 225-98.
Dollr P, Tu Z, Perona P. Integral channel features. In: Cavallaro A, Prince S, Alexander D, Eds. Proceedings of the British Machine Conference. London, UK. 2009. pp. 91.1-11
Breiman L. Random forests. Mach Learn 2001; 45(1): 5-32.
Starck JL, Elad M, Donoho DL. Image decomposition via the combination of sparse representations and a variational approach. IEEE Trans Image Process 2005; 14(10): 1570-82.
[] [PMID: 16238062]
Kutyniok G, Lemvig J, Lim WQ. Compactly supported shearlets Approximation theory XIII: San Antonio 2010 New York: Springer 2012; pp 163-86
Otsu N. A threshold selection method from gray-scale histogram. IEEE Trans Syst Man Cybern 1979; 9(1): 62-6.
Owen CG, Rudnicka AR, Mullen R, et al. Measuring retinal vessel tortuosity in 10-year-old children: validation of the Computer-Assisted Image Analysis of the Retina (CAIAR) program. Invest Ophthalmol Vis Sci 2009; 50(5): 2004-10.
[] [PMID: 19324866]
Kolmogorov V, Boykov Y. Hat metrics can be approximated by geo-cuts, or global optimization of length/area and flux. In: Proceedings of 10th IEEE International Conference on Computer Vision (ICCV). 2005 Oct 17-21; Beijing, China: IEEE pp 564-71.
Perona P, Malik P. Scale-space and edge detection using anisotropic diffusion. IEEE Trans Pattern Anal Mach Intell 1990; 12(7): 629-39.
Forkert ND, Schmidt-Richberg A, Fiehler J, et al. 3D cerebrovascular segmentation combining fuzzy vessel enhancement and level-sets with anisotropic energy weights. Magn Reson Imaging 2013; 31(2): 262-71.
[] [PMID: 22917500]
Mendonça AM, Campilho A. Segmentation of retinal blood vessels by combining the detection of centerlines and morphological reconstruction. IEEE Trans Med Imaging 2006; 25(9): 1200-13.
[] [PMID: 16967805]
Mendonça AM, Dashtbozorg B, Campilho A. Segmentation of the vascular network of the retina Image Analysis and Modeling in Ophthalmology. Florida: CRC Press, Taylor & Francis Group 2014; pp. 85-110.
Kovesi P. Symmetry and asymmetry from local phase. In: Proceedings of 10th Australian Joint Converence on Artifical Intelligence. 1997 Dec 2-4; Perth, Australia: IEEE pp 185-90.
Kovesi P. Image Features from Phase Congruency. Videre J Comput Vis Res 1999; 1(3): 2-26.
Maji D, Santara A, Mitra P, Sheet D. Ensemble of deep convolutional neural networks for learning to detect retinal vessels in fundus images 2016. arXiv:1603.04833 [cs.LG].
Köhler T, Budai A, Kraus MF, Odstrčilik J, Michelson G, Hornegger J. Automatic no-reference quality assessment for retinal fundus images using vessel segmentation. In: Proceedings of 26th IEEE International Symposium on Computer Based Medical Systems. 2013 Jun 20-22; Porto, Portugal: IEEE pp 95-100.
Hu K, Zhang Z, Niu X, Zhang Y, Cao C, Xiao F, et al. Retinal vessel segmentation of color fundus images using multiscale convolutional neural network with an improved cross-entropy loss function. Neurocomput 2018; 309: 179-91.
Vincent P, Larochelle H, Lajoie IBY, Manzagol PA. Stacked Denoising Autoencoders: Learning useful representations in a deep network with a local denoising criterion. J Mach Learn Res 2010; 11: 3371-408.
Bengio Y, Courville A, Vincent P. Representation learning: a review and new perspectives. IEEE Trans Pattern Anal Mach Intell 2013; 35(8): 1798-828.
[] [PMID: 23787338]
Nair V, Hinton GE. Rectified linear units improve restricted Boltzmann machines. In: Proceedings of 27th International Conference on Machine Learning. 2010 Jun 21-24; Haifa, Israel: IEEE pp 807-14.
Srivastava N, Hinton G, Krizhevsky A, Sutskever I, Salakhutdinov R. Dropout: A simple way to prevent neural networks from overfitting. J Mach Learn Res 2014; 15(1): 1929-58.
Bakir G, Hofmann T, Schölkopf B, Smola AJ, Taskar B, Vishwanathan SVN. Predicting structured data. Cambridge, MA: MIT Press 2007.
Rabiner LR, McClellan JH, Parks TW. FIR digital filter design techniques using weighted chebyshev approximation. Proc IEEE 1975; 4: 595-610.
Sinthanayothin C, Boyce JF, Cook HL, Williamson TH. Automated localisation of the optic disc, fovea, and retinal blood vessels from digital colour fundus images. Br J Ophthalmol 1999; 83(8): 902-10.
[] [PMID: 10413690]
Basit A, Fraz MM. Optic disc detection and boundary extraction in retinal images. Appl Opt 2015; 54(11): 3440-7.
[] [PMID: 25967336]
Winder RJ, Morrow PJ, McRitchie IN, Bailie JR, Hart PM. Algorithms for digital image processing in diabetic retinopathy. Comput Med Imaging Graph 2009; 33(8): 608-22.
[] [PMID: 19616920]
Joshi GD, Sivaswamy J, Krishnadas SR. Optic disk and cup segmentation from monocular color retinal images for glaucoma assessment. IEEE Trans Med Imaging 2011; 30(6): 1192-205.
[] [PMID: 21536531]
Kauppi T, Kalesnykiene V, Kamarainen JK, et al. DIARETDB0: Evaluation database and meth-odology for diabetic retinopathy algorithms. Technical report 2006.
Carmona EJ, Rincón M, García-Feijoó J, Martínez-de-la-Casa JM. Identification of the optic nerve head with genetic algorithms. Artif Intell Med 2008; 43(3): 243-59.
[] [PMID: 18534830]
Lowell J, Hunter A, Steel D, et al. Optic nerve head segmentation. IEEE Trans Med Imaging 2004; 23(2): 256-64.
[] [PMID: 14964569]
Decencière E, Zhang X, Cazuguel G, Lay B, Cochener B, Trone C, et al. Feedback on a publicly distributed image database: The Messidor database. Image Anal Stereol 2014; 33(3): 231-4.
Niemeijer M, van Ginneken B, Cree MJ, et al. Retinopathy online challenge: automatic detection of microaneurysms in digital color fundus photographs. IEEE Trans Med Imaging 2010; 29(1): 185-95.
[] [PMID: 19822469]
Kaur J, Sinha H. Automated localization of optic disc and macula from fundus images. Int J Adv Res Comput Sci Softw Eng 2012; 2(4): 242-9.
Quellec G, Lamard M, Josselin PM, Cazuguel G, Cochener B, Roux C. Optimal wavelet transform for the detection of microaneurysms in retina photographs. IEEE Trans Med Imaging 2008; 27(9): 1230-41.
[] [PMID: 18779064]
Niemeijer M, Xu X, Dumitrescu AV, et al. Automated measurement of the arteriolar-to-venular width ratio in digital color fundus photographs. IEEE Trans Med Imaging 2011; 30(11): 1941-50.
[] [PMID: 21690008]
Vezhnevets V, Konouchine V. GrowCut: Interactive multi-label N-D image segmentation by cellular automata. In: Proceedings of 15th International Conference on Computer Graphics and Applications (GraphiCon’2005). 2005 June 20-24; Moscow, USSR: IEEE pp 150-56.
Lowe DG. Distinctive image features from scale-invariant keypoints. Int J Comput Vis 2004; 60(2): 91-110.
Sauvola J, Pietikäinen M. Adaptive document image binarization. Pattern Recognit 2000; 33(2): 225-36.
Mushrif MM, Ray AK. A-IFS Histon based multi thresholding algorithm for color image segmentation. IEEE Signal Process Lett 2009; 16(3): 168-71.
Suzuki K. Overview of deep learning in medical imaging. Radiological Phys Technol 2017; 10(3): 257-73.
[] [PMID: 28689314]
Hinton GE, Osindero S, Teh YW. A fast learning algorithm for deep belief nets. Neural Comput 2006; 18(7): 1527-54.
[] [PMID: 16764513]
Rawat W, Wang Z. Deep convolutional neural networks for image classification: A comprehensive review. Neural Comput 2017; 29(9): 2352-449.
[] [PMID: 28599112]
Lecun Y, Bottou L, Bengio Y, Haffner P. Gradient-based learning applied to document recognition. Proc IEEE 1998; 86(11): 2278-4.
Krizhevsky A, Sutskever I, Hinton GE. Imagenet classification with deep convolutional neural networks. Commun ACM 2012; 60(6): 1097-105.
Szegedy C, Liu W, Jia Y, et al. Going deeper with convolutions. 2014. arXiv:1409.4842 [cs.CV].
Shin HC, Roth HR, Gao M, et al. Deep convolutional neural networks for computer-aided detection: CNN architectures, dataset characteristics, and Transfer Learning. IEEE Trans Med Imaging 2016; 35(5): 1285-98.
[] [PMID: 26886976]
Tajbakhsh N, Shin JYR, Gurudu SR, et al. Convolutional neural networks for medical image analysis: Full training or fine Tuning? IEEE Trans Med Imaging 2016; 35(5): 1299-312.
[] [PMID: 26978662]

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Year: 2020
Published on: 18 February, 2019
Page: [397 - 426]
Pages: 30
DOI: 10.2174/1573405615666190219102427
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