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Neural Computing and Applications
Erschienen in: Neural Computing and Applications 5/2016

01.07.2016 | EANN

Automatic detection of microaneurysms in colour fundus images for diabetic retinopathy screening

verfasst von: Sarni Suhaila Rahim, Chrisina Jayne, Vasile Palade, James Shuttleworth

Erschienen in: Neural Computing and Applications | Ausgabe 5/2016

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Abstract

Regular eye screening is essential for the early detection and treatment of the diabetic retinopathy. This paper presents a novel automatic screening system for diabetic retinopathy that focuses on the detection of the earliest visible signs of retinopathy, which are microaneurysms. Microaneurysms are small dots on the retina, formed by ballooning out of a weak part of the capillary wall. The detection of the microaneurysms at an early stage is vital, and it is the first step in preventing the diabetic retinopathy. The paper first explores the existing systems and applications related to diabetic retinopathy screening, with a focus on the microaneurysm detection methods. The proposed decision support system consists of an automatic acquisition, screening and classification of diabetic retinopathy colour fundus images, which could assist in the detection and management of the diabetic retinopathy. Several feature extraction methods and the circular Hough transform have been employed in the proposed microaneurysm detection system, alongside the fuzzy histogram equalisation method. The latter method has been applied in the preprocessing stage of the diabetic retinopathy eye fundus images and provided improved results for detecting the microaneurysms.
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Literatur
1.
Ministry of Health Malaysia Diabetic Retinopathy Screening Team (2012) Diabetes mellitus and complications—Module 2-2012. Ministry of Health Malaysia, Putrajaya
2.
3.
Health Technology Assessment Unit, Medical Development Division, Ministry of Health Malaysia (2002) Report screening for diabetic retinopathy. Ministry of Health Malaysia, Kuala Lumpur
4.
5.
Wild S, Roglic G, Green A, Sicree R, King H (2004) Global prevalence of diabetes estimates: estimates for the year 2000 and projections for 2030. Diabetes Care 27(5):1047–1053CrossRef
6.
Kauppi T, Kalesnykiene V, Kamarainen JK, Lensu L, Sorri, I, Uusitalo H, Kalviainen H, Pietila J (2006) DIARETDB0: evaluation database and methodology for diabetic retinopathy algorithms. Technical report
7.
Taylor R, Batey D (2012) Handbook of retinal screening in diabetes: diagnosis and management. Wiley, EnglandCrossRef
8.
Abdelazeem S (2002) Microaneurysm detection using vessels removal and circular Hough transform. In: Nineteenth national radio science conference. Alexandria, 19–21 Mar 2002
9.
Duda DO, Hart PE (1972) Use of the Hough transformation to detect lines and curves in pictures. Commun ACM 15(1):11–15CrossRefMATH
10.
Amiri SA, Hassanpour H, Shahiri M, Ghaderi R (2008) Detection of microaneurysms in retinal angiography images using the circular Hough transform. J Adv Comput Res 3(1):1–12
11.
Hipwell JH, Strachant F, Olson JA, McHardy KC, Sharp PF, Forrester JV (2000) Automated detection of microaneurysms in digital red-free photographs: a diabetic retinopathy screening tool. Diabet Med 17:588–594CrossRef
12.
Antal B, Hajdu A (2010) Improving microaneurysms detection in colour fundus images by using optimal combination of preprocessing methods and candidate extractors. In: 18th European signal processing conference. Aalborg, Denmark, 23–27 Aug 2010
13.
Niemeijer M, van Ginnerken B, Cree MJ, Mizutani A, Quellec G, Sanchez CI, Zhang B, Hornero R, Lamard M, Muramatsu C, Wu X, Cazuquel G, You J, Mayo A, Li Q, Hatanaka Y, Cochener B, Roux C, Karray F, Garcia M, Fujita H, Abramoff MD (2010) Retinopathy online challenge: automatic detection of microaneurysms in digital color fundus photographs. IEEE Trans Med Imaging 29(1):185–195. doi:10.​1109/​TMI.​2009.​2033909 CrossRef
14.
Quellec G, Lamard M, Josselin PM, Cazuguel G, Cochener B, Roux C (2008) Optimal wavelet transform for the detection of microaneurysms in retina photographs. IEEE Trans Med Imaging 27(9):1230–1241. doi:10.​1109/​TMI.​2008.​920619 CrossRef
15.
Sanchez CI, Hornero R, Mayo A, Garcia M (2009) Mixture model-based clustering and logistic regression for automatic detection of microaneurysms in retinal images. Med Imaging 10(1117/12):812088
16.
Zhang B, Wu X, You J, Li Q, Karray F (2009) Hierarchical detection of red lesions in retinal images by multiscale correlation filtering. Med Imaging. doi:10.​1117/​12.​813913
17.
Mizutani A, Muramatsu C, Hatanaka Y (2009) Automated microaneurysm detection method based on double-ring filter in retinal fundus images. Med Imaging. doi:10.​1117/​12.​813468
18.
Adal K, Ali S, Sidibe D, Karnowski T, Chaum E, Meriaudeau F (2013) Automated detection of microaneurysms using robust blob descriptors. SPIE Med Imaging 10(1117/12):2007913
19.
Lazar I, Hajdu A (2010) Retinal microaneurysm detection based on intensity profile analysis. In: Proceedings of the 8th international conference on applied informatics. Eger, Hungary, 27–30 Jan 2010
20.
Hatanaka Y, Inoue T, Okumura S, Muramatsu C, Fujita H (2012) Automated microaneurysm detection method based on double-ring filter and feature analysis in retinal fundus images. IEEE Symp Comput Based Med Syst. doi:10.​1109/​CBMS.​2012.​6266339
21.
Rahim SS, Palade V, Shuttleworth J, Jayne C (2014) Automatic screening and classification of diabetic retinopathy fundus images. In: Mladenov V et al (eds) Engineering applications of neural networks. Communications in computer and information science 459. Springer, Heidelberg, pp 113–122
22.
Kauppi T, Kalesnykiene V, Kamarainen JK, Lensu L, Sorri I, Raninen A, Voutilainen R, Uusitalo H, Kalviainen H, Pietila J (2007) DIARETDB1 diabetic retinopathy database and evaluation protocol. Technical report
23.
Sopharak A, Uyyanonvara B, Barman S (2013) Automated microaneurysm detection algorithms applied to diabetic retinopathy retinal images. Maejo Int J Sci Technol 7(2):294–314
24.
Duin RPW, Juszczak P, Paclik P, Pekalska E, de Ridder D, Tax DMJ, Verzakov S (2007) PRTools4.1, a matlab toolbox for pattern recognition. Delft University of Technology, Delft
25.
Ministry of Health Malaysia Diabetic Retinopathy Screening Team (2012) Handbook guide to diabetic retinopathy screening—Module 5-2012. Ministry of Health Malaysia, Putrajaya
26.
27.
28.
Noronha K, Nayak J, Bhat SN (2006) Enhancement of retinal fundus image to highlight the features for detection of abnormal eyes. In: Proceedings of the IEEE region 10 conference (TENCON2006). Hong Kong, China, 14–17 Nov 2006
29.
Larsen N, Godt J, Grunkin M, Lund-Andersen H, Larsen M (2003) Automated detection of diabetic retinopathy in a fundus photographic screening population. Invest Ophthalmol Vis Sci 44(2):767–771. doi:10.​1167/​iovs.​02-0417 CrossRef
30.
31.
Priya R, Aruna P (2011) Review of automated diagnosis of diabetic retinopathy using the support vector machine. Int J Appl Eng Res 1(4):844–863
32.
Priya R, Aruna P (2012) SVM and neural network based diagnosis of diabetic retinopathy. Int J Comput Appl 41(1):6–12. doi:10.​5120/​5503-7503
33.
Priya R, Aruna P, Suriya R (2013) Image analysis technique for detecting diabetic retinopathy. Int J Comput Appl 1:34–38
34.
Perumalsamy N, Sathya S, Prasad NM, Ramasamy K (2007) Software for reading and grading diabetic retinopathy. Aravind Diabetic Retinopathy Screening 3.0. Diabetes Care 30(9):2302–2306. doi:10.​2337/​dc07-0225 CrossRef
35.
Selvathi D, Prakash NB, Balagopal N (2012) Automated detection of diabetic retinopathy for early diagnosis using feature extraction and support vector machine. Int J Emerg Technol Adv Eng 2(11):762–767
36.
Scanlon PH, Wilkinson CP, Aldington SJ, Matthews DR (2009) A practical manual of diabetic retinopathy management. Wiley, ChicesterCrossRef
37.
Abramoff MD, Niemeijer M (2015) Mass screening of diabetic retinopathy using automated methods. Teleophthalmology in preventive medicine. Springer, Heidelberg, pp 41–50
38.
Zhang X, Thibault G, Decenciere E, Marcotegui B, Lay B, Danno R, Cazuguel G, Quellec G, Lamard M, Massin P, Chabouis Victor Z, Erginay A (2014) Exudate detection in color retinal images for mass screening of diabetic retinopathy. Med Image Anal 18(7):1026–1043. doi:10.​1016/​j.​media.​2014.​05.​004 CrossRef
39.
Holzinger A (2014) Trends in interactive knowledge discovery for personalized medicine: cognitive science meets machine learning. IEEE Intell Inform Bull 15(1):6–14
40.
Donsa K, Spat S, Beck P, Pieber TR, Holzinger A (2015) Towards personalization of diabetes therapy using computerized decision support and machine learning: some open problems and challenges. In: Holzinger A et al (eds) Smart health. Lecture notes in computer science LNCS 8700. Springer, Heidelberg, pp 237–260. doi:10.​1007/​978-3-319-16226-3-10
41.
42.
Garud H, Sheet D, Suveer A, Karri PK, Ray AK, Mahadevappa M, Chatterjee J (2011) Brightness preserving contrast enhancement in digital pathology. In: Proceedings of the 2011 international conference on image information processing (ICIIP 2011). Himachal Pradesh, India, 3–5 Nov 2011
43.
Adal KM, Sidibe D, Ali S, Chaum E, Karnowski TP, Meriaudeau F (2014) Automated detection of microaneurysms using scale-adapted blob analysis and semi-supervised learning. Comput Methods Programs Biomed 114:1–10. doi:10.​1016/​j.​cmpb.​2013.​12.​009 CrossRef
44.
45.
46.
Prakash J, Sumanthi K (2013) Detection and classification of microaneurysms for diabetic retinopathy. Int J Eng Res Appl 31–36
47.
Aravind C, Ponnibala M, Vijayachitra S (2013) Automatic detection of microaneurysms and classification of diabetic retinopathy images using SVM technique. IJCA Proc Int Conf Innov Intell Instrum Optim Electr Sci ICIIIOES 11:18–22
48.
Wisaeng K, Hiransakolwong N, Pothiruk E (2013) Automatic detection of exudates in digital retinal images. Int J Comput Appl 64(4):19–26. doi:10.​5120/​10622-5342
49.
Harangi B, Antal B, Hajdu A (2012) Automatic exudate detection with improved Naïve-Bayes classifier. In: Proceedings of the 25th IEEE international symposium on computer-based medical systems (CBMS). Roma, Italy, 20–22 June 2012
50.
51.
52.
Akram MU, Jamal I, Tariq A, Imtiaz, J (2012) Automated segmentation of blood vessels for detection of proliferative diabetic retinopathy. In: Proceedings of the IEEE–EMBS international conference on biomedical and health informatics (BJI 2012). Hong Kong and Shenzhen, China, 2–7 January 2012
53.
54.
Lim G, Lee ML, Hsu W, Wong TY (2014) Transformed representations for convolutional neural networks in diabetic retinopathy screening. Mod Artif Intell Health Anal 21–25
55.
Sundhar C, Archana D (2014) Automatic screening of fundus images for detection of diabetic retinopathy. Int J Commun Comput Technol 2(1):100–105
56.
Lichode RV, Kulkarni PS (2013) Automatic diagnosis of diabetic retinopathy by hybrid multilayer feed forward neural network. Int J Sci Eng Technol Res 2(9):1727–1733
57.
Selvathi D, Prakash NB, Balagopal N (2012) Automated detection of diabetic retinopathy for early diagnosis using feature extraction and support vector machine. Int J Emerg Technol Adv Eng 2(11):762–767
Metadaten
Titel
Automatic detection of microaneurysms in colour fundus images for diabetic retinopathy screening
verfasst von
Sarni Suhaila Rahim
Chrisina Jayne
Vasile Palade
James Shuttleworth
Publikationsdatum
01.07.2016
Verlag
Springer London
Erschienen in
Neural Computing and Applications / Ausgabe 5/2016
Print ISSN: 0941-0643
Elektronische ISSN: 1433-3058
DOI
https://doi.org/10.1007/s00521-015-1929-5

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