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RETRACTED ARTICLE: Exudate characterization to diagnose diabetic retinopathy using generalized method

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This article was retracted on 07 June 2022

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Abstract

Globally in recent days, the potential risk for patients with diabetes mellitus is the prevalence of diabetic retinopathy, which is a silent disease with no early symptoms and is the imperative cause of vision loss. An early diagnosis can be used to prevent for visual loss and blindness. In the regular screening process, assistance of computerized diagnosis can considerably minimize an ophthalmologists work and improve inter and intra viewer variability. A generalized method of semi automatic exudates characterization to diagnose diabetic retinopathy with exudates screening system of retinal image is presented in this paper. This system uses morphological processing based retinal blood vessel suppression, Semi automatic masking of optic disc structure and morphological component analysis based texture enhancement followed by segmentation and Adaptive Neuro-Fuzzy Inference System (ANFIS) based classification method to discriminate between normal and pathological retinal structures. The novelty of this system relies on the appropriate sequential application of exclusive image processing techniques in synergy with ANFIS classifier to improve the accuracy of exudate lesions characterization. The performance of the system has been evaluated by comparing it with various state of the art existing methods in terms of several performance metrics such as Accuracy, Average error rate, F-Score and Kappa value. The obtained numerical results prove that the proposed system with ANFIS classifier demonstrated superior performance in identification of exudate lesions.

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References

  • Abdulshahed AM, Longstaff AP, Fletcher S (2015) The application of ANFIS prediction models for thermal error compensation on CNC machine tools. Appl Soft Comput 27:158–168

    Google Scholar 

  • Acharya UR, Ng EYK, Tan JH, Sree SV, Ng KH (2012) An integrated index for the identification of diabetic retinopathy stages using texture parameters. J Med Syst 36(3):2011–2020

    Google Scholar 

  • Agurto C, Murray V, Barriga E, Murillo S, Pattichis M, Davis H, Soliz P (2010) Multiscale AM-FM methods for diabetic retinopathy lesion detection. IEEE Trans Med Imaging 29(2):502–512

    Google Scholar 

  • Agurto C, Murray V, Yu H, Wigdahl J, Pattichis M, Nemeth S, Barriga ES, Soliz P (2014) A multi scale optimization approach to detect exudates in the macula. IEEE J Biomed Health Inform 18:1328–1336

    Google Scholar 

  • Amadasun M, King R (1989) Textural features corresponding to textural properties. IEEE Trans Syst Man Cybern 19(5):1264–1274

    Google Scholar 

  • Amin J, Sharif M, Yasmin M, Ali H, Fernandes SL (2017) A method for the detection and classification of diabetic retinopathy using structural predictors of bright lesions. J Comput Sci 19:153–164

    Google Scholar 

  • Aqeel AF (2014) Automated algorithm for retinal image exudates and drusens detection, segmentation and measurement. In: IEEE international conference on electro/information technology, pp 206–215

  • Asha PR, Karpagavalli S (2015) Diabetic retinal exudates detection using machine learning techniques. In: Advanced computing and communication systems, international conference on, pp 1–5

  • Burt PJ, Adelson EH (1983) The Laplacian pyramid as a compact image code. IEEE Trans Commun 31:532–540

    Google Scholar 

  • Chand CR, Dheeba J (2015) Automatic detection of exudates in color fundus retinopathy images. Indian J Sci Technol 8:1–6

    Google Scholar 

  • da Cunha L, Arthur JZ, Do Minh N (2006) The non sub sampled contourlet transform: theory, design, and applications. IEEE Trans Image Proc 15:3089–3101

    Google Scholar 

  • Donate JP, Cortez P, SáNchez GG, De Miguel AS (2013) Time series forecasting using a weighted cross-validation evolutionary artificial neural network ensemble. Neurocomputing 109:27–32

    Google Scholar 

  • Hatanaka Y, Nakagawa T, Hayashi Y, Hara T, Fujita H (2008) Improvement of automatic hemorrhages detection methods using brightness correction on fundus images. In: 30th Annual international IEEE EMBS conference Vancouver, British Columbia, Canada, August 20–24, p 5429

  • Heneghan C, Flynn J, O’Keefe M, Cahill M (2002) Characterization of changes in blood vessel width and tortuosity in retinopathy of prematurity using image analysis. Med Image Anal 6(4):407–429

    Google Scholar 

  • Hossin M, Sulaiman MN (2015) A review on evaluation metrics for data classification evaluations. Int J Data Min Knowl Manag Process 5(2):1

    Google Scholar 

  • Imani E, Pourreza HR, Banaee T (2015) Fully automated diabetic retinopathy screening using morphological component analysis. Comput Med Imaging Graph 43:78–88

    Google Scholar 

  • Jain AB, Prakash VJ, Bhende M (2015) Techniques of fundus imaging. Med Vis Res Found 33(2):100

    Google Scholar 

  • Joshi S, Karule PT (2018) A review on exudates detection methods for diabetic retinopathy. Biomed Pharmacother 97:1454

    Google Scholar 

  • Kayal D, Banerjee S (2014) A new dynamic thresholding based technique for detection of hard exudates in digital retinal fundus image. In: 2014 International conference on signal processing and integrated networks, p 141

  • Klein R, Klein BEK, Moss SE (1984) Visual impairment in diabetes. Ophthalmology 91(1):1

    Google Scholar 

  • Kohavi R (1995) A study of cross-validation and bootstrap for accuracy estimation and model selection. Int Jt Conf Artif Intell 14(2):1137–1145

    Google Scholar 

  • Kohavi RF (1998) Provost: glossary of terms. Mach Learn 30(2/3):271–274

    Google Scholar 

  • Li HK, Ma Horton, Bursell S-E, Cavallerano J, Zimmer-Galler I, Tennant M (2011) Telehealth practice recommendations for diabetic retinopathy. 2nd edition. Telemed e-Health 17(10):814

    Google Scholar 

  • Long S, Huang X, Chen Z, Pardhan S, Zheng D (2019) Automatic detection of hard exudates in color retinal images using dynamic threshold and SVM classification: algorithm development and evaluation. Hindawi BioMed Res Int 2019:1–13

    Google Scholar 

  • Mookiah MRK, Acharya UR, Martis RJ, Chua CK, Lim CM, Ng EYK (2013) Evolutionary algorithm based classifier parameter tuning for automatic diabetic retinopathy grading: a hybrid feature extraction approach. Knowl Based Syst 39(2):9

    Google Scholar 

  • Nayak J, Bhat PS, Acharya R, Lim CM, Kagathi M (2008) Automated identification of diabetic retinopathy stages using digital fundus images. J Med Syst 32:107–115

    Google Scholar 

  • Olson GB, Cohen M (2019) A general mechanism of martensitic nucleation: part II. FCC → BCC and other martensitic transformations. Ambient Intell Humaniz Comput 10:1897–1914

    Google Scholar 

  • Quellec G, Lamard M, Josselin PM, Cazuguel G, Cochener B, Roux C (2008) Optimal wavelet transform for the detection of microaneurysms in retinal photographs. IEEE Trans Med Imaging 27(9):1230

    Google Scholar 

  • Rokade PM, Manza RR (2015) Automatic detection of hard exudates in retinal images using haar wavelet transform. Eye 4:402–410

    Google Scholar 

  • Ronald PC, Peng TK (2003) A text book of clinical ophthalmology: a practical guide to disorders of the eyes and their management, 3rd edn. World Scientific Publishing Company, Singapore

    Google Scholar 

  • Ruggeri A, Forrachia M, Grisan E (2003) Detecting the optic disc in retinal images by means of a geometrical model of vessel network. In: Proceedings of the 25th annual international conference of the IEEE engineering in medicine and biology society (IEEE Cat. No. 03CH37439). IEEE, vol 1, pp 902–905

  • Saeys Y, Inza I, Larrañaga P (2007) A review of feature selection techniques in bioinformatics. Bioinformatics 23(19):2507–2517

    Google Scholar 

  • Saleh MD, Eswaran C (2012) An automated decision-support system for non-proliferative diabetic retinopathy disease based on MAs and HAs detection. J Comput Methods Progr Biomed 108(1):186

    Google Scholar 

  • Senapati RK (2016) Bright lesion detection in color fundus images based on texture features. Bull Electr Eng Inform 5:92–100

    Google Scholar 

  • Sinthanayothin C, Boyce JF, Williamson TH, Cook HK, Mensah E, Lal S, Usher D (2002) Automated detection of diabetic retinopathy on digital fundus images. Diabet Med 19(2):105

    Google Scholar 

  • Sjolie AK, Stephenson J, Aldington S, Kohner E, Janka H, Stevens L, Fuller J (1997) EURODIAB Complications Study Group, retinopathy and vision loss in insulin-dependent diabetes in Europe. Ophthalmology 104(2):252

    Google Scholar 

  • Sopharak A, Uyyanonvara B, Barman S (2009) Automatic exudate detection from non-dilated diabetic retinopathy retinal images using fuzzy c-means clustering. Sensors 9:2148–2161

    Google Scholar 

  • Sridevi S, Nirmala S (2016) ANFIS based decision support system for prenatal detection of Truncus Arteriosus congenital heart defect. Appl Soft Comput 46:577–587

    Google Scholar 

  • Starck J-L, Elad M, Donoho D (2004) Redundant multiscale transforms and their application for morphological component separation. Adv Imaging Electron Phys 132:287–348

    Google Scholar 

  • Starck J-L, Elad M, Donoho DL (2005) Image decomposition via the combination of sparse representations and a variational approach. Image Proc IEEE Trans 14:1570–1582

    MathSciNet  MATH  Google Scholar 

  • Usher D, Dumsky M, Himaga M, Williamson T, Nussey S, Boyce J (2004) Automated detection of diabetic retinopathy in digital retinal images: a tool for diabetic retinopathy screening. Diabet Med 21(1):84

    Google Scholar 

  • Winder RJ, Morrow PJ, McRitchie IN, Bailie JR, Hart PM (2009) Algorithms for digital image processing in diabetic retinopathy. Comput Med Imaging Graph 33(8):608–622

    Google Scholar 

  • Wisaeng K, Hiransakolwong N, Pothiruk E (2015) Automatic detection of exudates in retinal images based on threshold moving average models. Biofizika 60(2):360

    Google Scholar 

  • Xinbo Gao WL, Tao Dacheng, Li Xuelong (2012) Image quality assessment based on multiscale geometric analysis. IEEE Trans Image Proc 2009(18):1409–1423

    MathSciNet  MATH  Google Scholar 

  • Youssef D, Solouma NH (2012) Accurate detection of blood vessels improves the detection of exudates in color fundus images. Comput Methods Progr Biomed 108(3):1052–1061. https://doi.org/10.1016/j.cmpb.2012.06.006

    Article  Google Scholar 

  • Zana F, Klein JC (1997) Robust segmentation of vessels from retinal angiography. In: Proceedings of international conference digital signal processing, Santorini, Greece, pp 1087–1091

  • Zana F, Klein JC (1999) A multimodal registration algorithm of eye fundus images using vessels detection and Hough transform. IEEE Trans Med Imaging 18:419–427

    Google Scholar 

  • Zhang Q, Guo B-l (2009) Multifocus image fusion using the nonsubsampled contourlettransform. Signal Proc 89(7):1334–1346

    MATH  Google Scholar 

Download references

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Correspondence to R. Valarmathi.

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This article has been retracted. Please see the retraction notice for more detail: https://doi.org/10.1007/s12652-022-04092-5"

The proposed works deals with the identification and grading of exudates lesions from retinal images and the images for this proposed work were selectively taken from STARE* and MESSIDOR* databases. Data are used for only the research purpose. Therefore there is no need to get declaration from the patient.

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Valarmathi, R., Saravanan, S. RETRACTED ARTICLE: Exudate characterization to diagnose diabetic retinopathy using generalized method. J Ambient Intell Human Comput 12, 3633–3645 (2021). https://doi.org/10.1007/s12652-019-01617-3

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  • DOI: https://doi.org/10.1007/s12652-019-01617-3

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