Automatic detection of microaneurysms in diabetic retinopathy fundus images using the L∗a∗b color space

Journal of the Optical Society of America A: Optics and Image Science, and Vision

Volumn 33, Issue 1, 2016, Pages 74-83

  Navarro, Pedro J ^a   Alonso, Diego ^a   Stathis, Kostas ^b  

^a TECHNICAL UNIVERSITY OF CARTAGENA   (Spain)

^b UNIVERSITY OF LONDON   (United Kingdom)

Author keywords

[No Author keywords available]

Indexed keywords

COLOR; DIAGNOSIS; EYE PROTECTION; IMAGE ENHANCEMENT; MEDICAL IMAGING; NEAREST NEIGHBOR SEARCH;

AUTOMATED IMAGE PROCESSING; AUTOMATIC DETECTION; AUTOMATIC IMAGE PROCESSING; DIABETIC PATIENT; DIABETIC RETINOPATHY; HIGH SENSITIVITY; K-NEAREST NEIGHBORS CLASSIFIERS; PUBLIC HEALTH SYSTEMS;

IMAGE PROCESSING;

ALGORITHM; AUTOMATION; COLOR; COMPLICATION; DIABETIC RETINOPATHY; DIAGNOSTIC IMAGING; EYE FUNDUS; IMAGE PROCESSING; MICROANEURYSM; PROCEDURES; RECEIVER OPERATING CHARACTERISTIC; WAVELET ANALYSIS;

ALGORITHMS; AUTOMATION; COLOR; DIABETIC RETINOPATHY; FUNDUS OCULI; IMAGE PROCESSING, COMPUTER-ASSISTED; MICROANEURYSM; ROC CURVE; WAVELET ANALYSIS;

EID: 84962193048     PISSN: 10847529     EISSN: 15208532     Source Type: Journal    
DOI: 10.1364/JOSAA.33.000074     Document Type: Article

References (28)

1. H. R. Taylor and J. E. Keeffe, "World blindness: a 21st century perspective", Br. J. Ophthalmol. 85, 261-266 (2001).
2. N. Cheung, P. Mitchell, and T. Y. Wong, "Diabetic retinopathy", Lancet 376, 124-136 (2010).
3. R. J. Winder, P. J. Morrow, I. N. McRitchie, J. R. Bailie, and P. M. Hart, "Algorithms for digital image processing in diabetic retinopathy", Comput. Med. Imaging Graph. 33, 608-622 (2009).
4. R. Welikala, J. Dehmeshki, A. Hoppe, V. Tah, S. Mann, T. Williamson, and S. Barman, "Automated detection of proliferative diabetic retinopathy using a modified line operator and dual classification", Comput. Methods Programs Biomed. 114, 247-261 (2014).
5. S. W. Franklin and S. E. Rajan, "Diagnosis of diabetic retinopathy by employing image processing technique to detect exudates in retinal images", IET Image Process. 8, 601-609 (2014).
6. N. Thomas, T. Mahesh, and K. Shunmuganathan, "Detection and classification of exudates in diabetic retinopathy", Int. J. Adv. Res. Comput. Sci. Manag. Stud. 2, 296-305 (2014).
7. X. Zhang, G. Thibault, E. Decencière, B. Marcotegui, B. Laÿ, R. Danno, G. Cazuguel, G. Quellec, M. Lamard, P. Massin, A. Chabouis, Z. Victor, and A. Erginay, "Exudate detection in color retinal images for mass screening of diabetic retinopathy", Med. Image Anal. 18, 1026-1043 (2014).
8. H. F. Jelinek, M. J. Cree, J. J. G. Leandro, J. V. B. Soares, R. M. Cesar, and A. Luckie, "Automated segmentation of retinal blood vessels and identification of proliferative diabetic retinopathy", J. Opt. Soc. Am. A 24, 1448-1456 (2007).
9. A. Sopharak, B. Uyyanonvara, and S. Barman, "Simple hybrid method for fine microaneurysm detection from non-dilated diabetic retinopathy retinal images", Comput. Med. Imaging Graph. 37, 394-402 (2013).
10. L. Tang, M. Niemeijer, J. M. Reinhardt, M. K. Garvin, and M. D. Abramoff, "Splat feature classification with application to retinal hemorrhage detection in fundus images", IEEE Trans. Med. Imaging 32, 364-375 (2013).
11. R. Casanova, S. Saldana, E. Y. Chew, R. P. Danis, C. M. Greven, and W. T. Ambrosius, "Application of random forests methods to diabetic retinopathy classification analyses", PLoS One 9, 1-8 (2014).
12. H. L. Tang, J. Goh, T. Peto, B. W. K. Ling, L. I. Alturk, Y. Hu, S. Wang, and G. M. Saleh, "The reading of components of diabetic retinopathy: an evolutionary approach for filtering normal digital fundus imaging in screening and population based studies", PLoS One 8, e66730 (2013).
13. S. Roychowdhury, D. Koozekanani, and K. Parhi, "DREAM: diabetic retinopathy analysis using machine learning", IEEE J. Biomed. Health Inform. 18, 1717-1728 (2014).
14. ∗ Colour Space. ISO 11664-4:2008 (E)/CIE S 014-4/E:2007", 2008, https://www.iso.org/obp/ui/# iso:std:iso: 11664:.4:ed.1:v1:en.
15. M. Niemeijer, B. Van Ginneken, J. Staal, M. S. A. Suttorp-Schulten, and M. D. Abràmoff, "Automatic detection of red lesions in digital color fundus photographs", IEEE Trans. Med. Imaging 24, 584-592 (2005).
16. E. Decencière, X. Zhang, G. Cazuguel, B. Lay, B. Cochener, C. Trone, P. Gain, R. Ordonez, P. Massin, A. Erginay, B. Charton, and J.-C. Klein, "Feedback on a publicly distributed image database: the MESSIDOR database", Image Anal. Stereol. 33, 231-234 (2014).
17. M. Unser, "Texture classification and segmentation using wavelet frames", IEEE Trans. Image Process. 4, 1549-1560 (1995).
18. A. Laine and J. Fan, "Texture classification by wavelet package signatures", IEEE Trans. Pattern Anal. Mach. Intell. 15, 1186-1191 (1993).
19. G. Quellec, M. Lamard, P. M. Josselin, G. Cazuguel, B. Cochener, and C. Roux, "Optimal wavelet transform for the detection of microaneurysms in retina photographs", IEEE Trans. Med. Imaging 27, 1230-1241 (2008).
20. R. C. Gonzalez and R. E. Woods, Digital Image Processing (Prentice Hall, 2007).
21. M. Sezgin and B. Sankur, "Selection of thresholding methods for non-desctructive testing applications", in Image Processing (IEEE, 2001), Vol. 3, pp. 764-767.
22. J. Lim, Two-Dimensional Signal and Image Processing (Prentice Hall, 1989).
23. A. Gavlasová, A. Procházka, and M. Mudrová, "Wavelet use for image classification", in 15th International Conference on Process Control (2005).
24. G. Lambert and F. Bock, "Wavelet methods for texture defect detection", in Proceedings of International Conference on Image Process (1997), Vol. 3, pp. 201-204.
25. S. G. Mallat, "A theory for multiresolution signal decomposition: the wavelet representation", IEEE Trans. Pattern Anal. Mach. Intell. 11, 674-693 (1989).
26. K. H. Ghazali, M. F. Mansor, M. M. Mustafa, and A. Hussain, "feature extraction technique using discrete wavelet transform for image classification", in 5th Student Conference on Research and Development (2007), pp. 1-4.
27. R. M. Haralick, "Statistical and structural approaches to texture", Proc. IEEE 67, 786-804 (1979).
28. C. Liu, "Learning the uncorrelated, independent, and discriminating color spaces for face recognition", IEEE Trans. Inf. Forensics Security 3, 213-222 (2008).