Identification of suitable fundus images using automated quality assessment methods

Journal of Biomedical Optics

Volumn 19, Issue 4, 2014, Pages

  Şevik, Uǧur ^a   Köse, Cemal ^a   Berber, Tolga ^a   Erdöl, Hidayet ^a  

^a KARADENIZ TECHNICAL UNIVERSITY   (Turkey)

Author keywords

automated image quality assessment; computer assisted image analysis; machine learning; retinal image analysis; suitable retinal image frame

Indexed keywords

AUTOMATION; DIAGNOSIS; EYE PROTECTION; GRADING; IMAGE ANALYSIS; IMAGE QUALITY; IMAGE SEGMENTATION; LEARNING SYSTEMS; STATISTICS;

COMPUTER-ASSISTED IMAGE ANALYSIS; CONSECUTIVE IMAGES; DIABETIC RETINOPATHY; IMAGE QUALITY ASSESSMENT; PRECISION AND RECALL; RETINAL IMAGE; RETINAL IMAGE ANALYSIS; RETINAL IMAGE QUALITY;

OPHTHALMOLOGY;

ARTIFICIAL INTELLIGENCE; COMPUTER ASSISTED DIAGNOSIS; DIABETIC RETINOPATHY; EYE FUNDUS; FACTUAL DATABASE; HUMAN; IMAGE PROCESSING; PATHOLOGY; PROCEDURES; RETINA; RETINA BLOOD VESSEL; STATISTICAL MODEL;

ARTIFICIAL INTELLIGENCE; DATABASES, FACTUAL; DIABETIC RETINOPATHY; FUNDUS OCULI; HUMANS; IMAGE INTERPRETATION, COMPUTER-ASSISTED; IMAGE PROCESSING, COMPUTER-ASSISTED; MODELS, STATISTICAL; RETINA; RETINAL VESSELS;

EID: 84898443105     PISSN: 10833668     EISSN: 15602281     Source Type: Journal    
DOI: 10.1117/1.JBO.19.4.046006     Document Type: Article

References (39)

1. C. Köse et al., "Simple methods for segmentation and measurement of diabetic retinopathy lesions in retinal fundus images," Comput. Methods Programs Biomed. 107(2), 274-293 (2012).
2. J. Paulus et al., "Automated quality assessment of retinal fundus photos," Int. J. Comput. Assist. Radiol. Surg. 5(6), 557-564 (2010).
3. A. D. Fleming et al., "Automated assessment of diabetic retinal image quality based on clarity and field definition," Invest. Ophthalmol. Vis. Sci. 47(3), 1120-1125 (2006). (Pubitemid 46768348)
4. L. Giancardo et al., "Elliptical local vessel density: a fast and robust quality metric for retinal images," in Conf. Proc. IEEE Eng. Med. Biol. Soc., Vol. 2008, pp. 3534-3537, IEEE, Vancouver, BC (2008).
5. M. Lalonde, L. Gagnon, and M. Boucher, "Automatic visual quality assessment in optical fundus images," in Proc. of Vision Interface, pp. 259-264, Canadian Image Processing and Pattern Recognition Society, Ottawa, Ontario, Canada (2001).
6. S. C. Lee and Y.Wang, "Automatic retinal image quality assessment and enhancement," Proc. SPIE 3661, 1581-1590 (1999).
7. M. Niemeijer, M. D. Abràmoff, and B. van Ginneken, "Image structure clustering for image quality verification of color retina images in diabetic retinopathy screening," Med. Image Anal. 10(6), 888-898 (2006). (Pubitemid 44815259)
8. J. Staal et al., "Ridge-based vessel segmentation in color images of the retina," IEEE Trans. Med. Imaging 23(4), 501-509 (2004).
9. T. Kauppi, "Eye fundus image analysis for automatic detection of diabetic retinopathy," Ph.D. Thesis, Diss. Lappeenranta University of Technology (2010).
10. Y. Wang, W. Tan, and S. C. Lee, "Illumination normalization of retinal images using sampling and interpolation," Proc. SPIE 4322, 500-507 (2001). (Pubitemid 32839848)
11. A. M. Reza, "Realization of the contrast limited adaptive histogram equalization (CLAHE) for real-time image enhancement," J. VLSI Signal Process. Signal, Image Video Technol. 38(1), 35-44 (2004).
12. F. Zana and J. C. Klein, "Segmentation of vessel-like patterns using mathematical morphology and curvature evaluation," IEEE Trans. Image Process. 10(7), 1010-1019 (2001). (Pubitemid 32650877)
13. S. Sekhar, W. Al-Nuaimy, and A. K. Nandi, "Automated localisation of retinal optic disk using Hough transform," in 2008 5th IEEE Int. Symp. Biomed. Imaging From Nano to Macro, pp. 1577-1580, IEEE, Paris, France (2008).
14. M. U. Akram et al., "Retinal images: optic disk localization," in Int. Conf. Image Anal. Recognit., pp. 40-49, Springer Berlin Heidelberg, Povoa de Varzim, Portugal (2010).
15. S. Liangwongsan and B. Marungsri, "Extracted circle hough transform and circle defect detection algorithm," World Acad. Sci. Eng. Technol. 5(12), 316-321 (2011).
16. C. Köse, U. Sevik, and O. Gençaliog?lu, "Automatic segmentation of age-related macular degeneration in retinal fundus images," Comput. Biol. Med. 38(5), 611-619 (2008).
17. C. Sinthanayothin et al., "Automated localisation of the optic disc, fovea, and retinal blood vessels from digital colour fundus images," Br. J. Ophthalmol. 83(8), 902-910 (1999). (Pubitemid 29376022)
18. J. Nayak, P. S. Bhat, and U. R. Acharya, "Automatic identification of diabetic maculopathy stages using fundus images," J. Med. Eng. Technol. 33(2), 119-129 (2009).
19. J. Nayak et al., "Automated diagnosis of glaucoma using digital fundus images," J. Med. Syst. 33(5), 337-346 (2009).
20. S.-K. Hwang andW.-Y. Kim, "A novel approach to the fast computation of Zernike moments," Pattern Recognit. 39(11), 2065-2076 (2006).
21. R. M. Haralick, K. Shanmugam, and I. H. Dinstein, "Textural features for image classification," IEEE Trans. Syst. Man Cybern. 3(6), 610-621 (1973).
22. B. S. Manjunath et al., "Color and texture descriptors," IEEE Trans. Circuits Syst. Video Technol. 11(6), 703-715 (2001). (Pubitemid 32616532)
23. D. K. Park, Y. S. Jeon, and C. S.Won, "Efficient use of local edge histogram descriptor," in Proc. 2000 ACM Work. Multimed., pp. 51-54, ACM Press, New York (2000).
24. C. S. Won, "Feature extraction and evaluation using edge histogram descriptor in MPEG-7," in Proc. Adv. Multimed. Inf. Process. Vol. 3333, pp. 583-590, Springer Berlin Heidelberg, Tokyo, Japan (2004).
25. I. Mierswa et al., "YALE: rapid prototyping for complex data mining tasks," in Proc. 12th ACM SIGKDD Int. Conf. on Knowledge Discovery and Data Mining, pp. 935-940, ACM Press, New York (2006). (Pubitemid 44535615)
26. S. Mika et al., "Fisher discriminant analysis with kernels," in Proc. 1999 IEEE Signal Process. Soc. Work., pp. 41-48, IEEE, Madison, WI (1999). (Pubitemid 30510358)
27. J. Yang and J. Yang, "Why can LDA be performed in PCA transformed space?," Pattern Recognit. 36(2), 563-566 (2003). (Pubitemid 35212848)
28. D. Bremner et al., "Output-sensitive algorithms for computing nearestneighbour decision boundaries," Discrete Comput. Geom. 33(4), 593-604 (2005). (Pubitemid 40550561)
29. H.K.D.H. Bhadeshia, "Neural networks in materials science," ISIJ Int. 39(10), 966-979 (1999). (Pubitemid 30515227)
30. H. Zhang, "The optimality of Naive Bayes," in Proc. 17th Int. FLAIRS Conf., Vol. 1(2), pp. 562-567, The AAAI Press, Menlo Park, CA (2004).
31. C. Cortes and V. Vapnik, "Support-vector networks," Mach. Learn. 20(3), 273-297 (1995).
32. W. Siedlecki and J. Sklansky, "A note on genetic algorithms for largescale feature selection," Pattern Recognit. Lett. 10(5), 335-347 (1989).
33. P. Pudil, J. Novovi?ová, and J. Kittler, "Floating search methods in feature selection," Pattern Recognit. Lett. 15(11), 1119-1125 (1994).
34. D.M.W. Powers, "Evaluation: from precision, recall and f-measure to roc., informedness, markedness & correlation," J. Mach. Learn. Technol. 2(1), 37-63 (2011).
35. Y.-H.Wen, A. Bainbridge-Smith, and A. B. Morris, "Automated assessment of diabetic retinal image quality based on blood vessel detection," in Proc. Image Vis. Comput., Vol. 36(1), pp. 132-136, Hamilton, New Zealand (2007).
36. H. Müller et al., "A review of content-based image retrieval systems in medical applications-clinical benefits and future directions," Int. J. Med. Inform. 73(1), 1-23 (2004). (Pubitemid 38229184)
37. N. A. Rosa et al., "Using relevance feedback to reduce the semantic gap in content-based image retrieval of mammographic masses," in Proc. 30th Annu. Int. Conf. IEEE Eng. Med. Biol. Soc., Vol. 2008, pp. 406-409, IEEE, Vancouver, BC (2008).
38. B. Zheng, "Computer-aided diagnosis in mammography using contentbased image retrieval approaches: current status and future perspectives," Algorithms 2(2), 828-849 (2009).
39. D. D. Feng, "Automatic medical image categorization and annotation using LBP and MPEG-7 edge histograms," in 2008 Int. Conf. Technology & Biomedical Application, pp. 51-53, IEEE, Shenzhen, China (2008).