A generalized method for the segmentation of exudates from pathological retinal fundus images

• Autorzy: Kaur J. , Mittal D.

Identyfikatory

DOI

10.1016/j.bbe.2017.10.003

• Języki publikacji: EN

Abstrakty

EN

Diabetic retinopathy, an asymptomatic complication of diabetes, is one of the leading causes of blindness in the world. The exudates, abnormal leaked fatty deposits on retina, are one of the most prevalent and earliest clinical signs of diabetic retinopathy. In this paper, a generalized exudates segmentation method to assist ophthalmologists for timely treatment and effective planning in the diagnosis of diabetic retinopathy is developed. The main contribution of the proposed method is the reliable segmentation of exudates using dynamic decision thresholding irrespective of associated heterogeneity, bright and faint edges. The method is robust in the sense that it selects the threshold value dynamically irrespective of the large variations in retinal fundus images from varying databases. Since no performance comparison of state of the art methods is available on common database, therefore, to make a fair comparison of the proposed method, this work has been performed on a diversified database having 1307 retinal fundus images of varying characteristics namely: location, shapes, color and sizes. The database comprises of 649 clinically acquired retinal fundus images from eye hospital and 658 retinal images from publicly available databases such as STARE, MESSIDOR, DIARETDB1 and e-Optha EX. The segmentation results are validated by performing two sets of experiments namely: lesion based evaluation criteria and image based evaluation criteria. Experimental results at lesion level show that the proposed method outperforms other existing methods with a mean sensitivity/specificity/accuracy of 88.85/96.15/93.46 on a composite database of retinal fundus images. The segmentation results for image-based evaluation with a mean sensitivity/specificity/accuracy of 94.62/ 98.64/96.74 respectively prove the clinical effectiveness of the method. Furthermore, the significant collective performance of these experiments on clinically as well as publicly available standard databases proves the generalization ability and the strong candidature of the proposed method in the real-time diagnosis of diabetic retinopathy.

Słowa kluczowe

EN

exudate segmentation; diabetic retinopathy; retinal fundus image; lesion; image based evaluation

PL

segmentacja wysięku; retinopatia cukrzycowa; obraz dna oka; zmiana chorobowa

• Wydawca: Nałęcz Institute of Biocybernetics and Biomedical Engineering of the Polish Academy of Sciences ; Elsevier
• Czasopismo: Biocybernetics and Biomedical Engineering
• Rocznik: 2018
• Tom: Vol. 38, no. 1
• Strony: 27--53
• Opis fizyczny: Bibliogr. 43 poz., rys., tab., wykr.

Twórcy

autor

Kaur J.

• Electrical and Instrumentation Engineering Department, Thapar University, Patiala 147004, India

autor

Mittal D.

• Electrical and Instrumentation Engineering Department, Thapar University, Patiala 147004, India

Bibliografia

• [1] Sarah W, Gojka R, Anders G, Richard S, Hilary K. Global prevalence of diabetes: estimates for the year 2000 and projection for 2030. Diabetes Care 2004;27:1047–53.
• [2] Chu J, Ali Y. Diabetic retinopathy. A review. Drug Dev Res 2008;69:1–14.
• [3] Frith P, Gray R, MacLennan S, Ambler P. The eye in clinical practice. 2nd ed. London: Blackwell Science Ltd.; 2001.
• [4] Hollyfield JG, Anderson RE, LaVail MM, editors. Retinal degenerative diseases, Springer. Laser photocoagulation: ocular research and therapy in diabetic retinopathy. 2006. pp. 195–200.
• [5] Somasundaram SK, Alli P. Diagnosing and ranking retinopathy disease level using diabetic fundus image recuperation approach. Int J Appl Eng Res 2014;9:30459–75.
• [6] Phillips R, Forrester J, Sharp P. Archive automated detection and quantification of retinal exudates; 1993;90–4.
• [7] Ege BM, Hejlesen OK, Larsen OV, Møller K, Jennings B, Kerr D, et al. Screening for diabetic retinopathy using computer based image analysis and statistical classification. Comput Methods Programs Biomed 2000;62:165–75.
• [8] Wang HWH, Hsu WHW, Goh KGGKG, Lee MLLML. An effective approach to detect lesions in color retinal images. Proc IEEE Conference on Computer Vision and Pattern Recognition CVPR 2000 (Cat. No. PR00662), vol. 2. 2000. pp. 1–6.
• [9] Sánchez CI, García M, Mayo A, López MI, Hornero R. Retinal image analysis based on mixture models to detect hard exudates. Med Image Anal 2009;13:650–8.
• [10] García M, Sánchez CI, López MI, Abásolo D, Hornero R. Neural network based detection of hard exudates in retinal images. Comput Methods Programs Biomed 2009;93:9–19.
• [11] Yazid H, Arof H, Isa HM. Automated identification of exudates and optic disc based on inverse surface thresholding. J Med Syst 2012;36:1997–2004.
• [12] Walter T, Klein J-C, Massin P, Erginay A. A contribution of image processing to the diagnosis of diabetic retinopathy – detection of exudates in color fundus images of the human retina. IEEE Trans Med Imaging 2002;21:1236–43.
• [13] Sopharak A, Uyyanonvara B, Barman S, Williamson TH. Automatic detection of diabetic retinopathy exudates from non-dilated retinal images using mathematical morphology methods. Comput Med Imaging Graph 2008;32:720–7.
• [14] Welfer D, Scharcanski J, Marinho DR. A coarse-to-fine strategy for automatically detecting exudates in color eye fundus images. Comput Med Imaging Graph 2010;34:228–35.
• [15] Zhang X, Thibault G, Decencière E, Marcotegui B, Laÿ B, Danno R, et al. Exudate detection in color retinal images for mass screening of diabetic retinopathy. Med Image Anal 2014;18:1026–43.
• [16] Sopharak A, Uyyanonvara B, Barman S. Automatic exudate detection from non-dilated diabetic retinopathy retinal images using fuzzy C-means clustering. Sensors 2009;9:2148–61.
• [17] Osareh A, Mirmehdi M, Thomas B, Markham R. Scientific report. Digital colour images: image (Rochester, NY); 2003;1220–3.
• [18] Niemeijer M, Ginneken BV, Russel SR, Suttorp-Schulten MSA, Abramoff MD. Automated detection and differentiation of Drusen, exudates, and cotton-wool spots in digital color fundus photographs for early diagnosis of diabetic retinopathy. Investig Ophthalmol Visual Sci 2007;48(5):2260–7.
• [19] Giancardo L, Meriaudeau F, Karnowski TP, Li Y, Garg S, Tobin KW, et al. Exudate-based diabetic macular edema detection in fundus images using publicly available datasets. Med Image Anal 2012;16:216–26.
• [20] 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.
• [21] Osareh A, Mirmehdi M. Automatic recognition of exudative maculopathy using fuzzy c-means clustering and neural networks. Med Image Understand Anal 2001;3:49–52.
• [22] Usher D, Dumskyj M, Himaga M, Williamson TH, Nussey S, Boyce J. Automated detection of diabetic retinopathy in digital retinal images: a tool for diabetic retinopathy screening. Diabetic Med 2004;21(1):84–90.
• [23] Reza AW, Eswaran C, Hati S. Automatic tracing of optic disc and exudates from color fundus images using fixed and variable thresholds. J Med Syst 2009;33:73–80.
• [24] Reza AW, Eswaran C, Dimyati K. Diagnosis of diabetic retinopathy. Automatic extraction of optic disc and exudates from retinal images using marker-controlled watershed transformation. J Med Syst 2011;35:1491–501.
• [25] Youssef D, Solouma NH. Accurate detection of blood vessels improves the detection of exudates in color fundus images. Comput Methods Programs Biomed 2012;108:1052–61.
• [26] Harangi B, Antal B, Hajdu A. Automatic exudate detection with improved Naive–Bayes classifier. Proceedings – IEEE Symposium on Computer-Based Medical Systems; 2012.
• [27] Rajput GG, Patil PN. Detection and classification of exudates using k-means clustering in color retinal images. Proceedings – 2014 5th International Conference on Signal and Image Processing, ICSIP 2014; 2014. p. 126–30.
• [28] Lahmiri S, Boukadoum M. Automated detection of circinate exudates in retina digital images using empirical mode decomposition and the entropy and uniformity of the intrinsic mode functions. Biomedizinische Technik 2014;59:357–66.
• [29] Wisaeng K, Hiransakolwong N, Pothiruk E. Automatic detection of exudates in retinal images based on threshold moving average models. Biophysics 2015;60:288–97.
• [30] Jaya T, Dheeba J, Singh NA. Detection of hard exudates in colour fundus images using fuzzy support vector machine-based expert system. J Digital Imaging 2015;28:761–8.
• [31] Kaur J, Mittal D. Segmentation and measurement of exudates in fundus images of the retina for detection of retinal disease. J Biomed Eng Med Imaging 2015;2(1):27–38.
• [32] Chugh S, Kaur J, Mittal D. Exudates segmentation in retinal fundus images for the detection of diabetic retinopathy. Int J Eng Res Technol 2014;3:673–7.
• [33] Mishra SK, Mittal D, Sunkaria RK. Designing of computer aided diagnostic system for the identification of exudates in retinal fundus images. J Biomed Eng Med Imaging 2015;2(3):29–40.
• [34] Mittal D, Kumari K. Automated detection and segmentation of Drusen in retinal fundus images. Comput Electr Eng 2015;47:82–95.
• [35] Kaur J, Mittal D. A generalized method for the detection of vascular structure in pathological retinal images. Biocybern Biomed Eng 2017;37:184–200.
• [36] Hoover A. Locating blood vessels in retinal images by piecewise threshold probing of a matched filter response. IEEE Trans Med Imaging 2000;19:203–10. http://cecas.clemson.edu/ahoover/stare/.
• [37] Decencière E, Zhang X, Cazuguel G, Laÿ B, Cochener B, Trone C, et al. Feedback on a publicly distributed image database: The Messidor database. Image Anal Stereol 2014;33:231–4. http://www.adcis.net/en/Download-Third-Party/Messidor. Html.
• [38] Kauppi T, Kalesnykiene V, Kamarainen J-K, Lensu L, Sorri I, Raninen A, et al. The DIARETDB1 diabetic retinopathy database and evaluation protocol. Proceedings of the British Machine Vision Conference, vol. 1; 2007. 15.1–10. http://www.it.lut.fi/project/imageret/diaretdb1/.
• [39] Decencière E, Cazuguel G, Zhang X, Thibault G, Klein JC, Meyer F, et al. TeleOphta: machine learning and image processing methods for teleophthalmology. IRBM 2013;34:196–203. http://www.adcis.net/en/Download-Third-Party/E-Ophtha. Html.
• [40] Liu Q, Zou B, Chen J, Ke W, Yue K, Chen Z, et al. A location-to-segmentation strategy for automatic exudate segmentation in colour retinal fundus images. Comput Med Imaging Graph 2017;55:78–86.
• [41] Figueiredo IN, Kumar S. Wavelet-based computer-aided detection of bright lesions in retinal fundus images. CompIMAGE 2014. Lect Notes Comput Sci 2014;8641:234–40.
• [42] Figueiredo IN, Kumar S, Oliveira CM, Ramos JD, Engquist B. Automated lesion detectors in retinal fundus images. Comput Biol Med 2015;66:47–65.
• [43] Levinshtein A, Stere A, Kutulakos K, Fleet D, Dickinson S, Siddiqi K. Turbopixels: fast superpixels using geometric flows. IEEE Trans Pattern Anal Mach Intell 2009;31:2290–7.

Uwagi

PL

Opracowanie rekordu w ramach umowy 509/P-DUN/2018 ze środków MNiSW przeznaczonych na działalność upowszechniającą naukę (2018).