A new approach to optic disc detection in human retinal images using the firefly algorithm

详细信息    查看全文

• 作者：Javad Rahebi ; Fırat Hardalaç
• 关键词：Firefly algorithm ; Optic disc detection ; Retinal images
• 刊名：Medical and Biological Engineering and Computing
• 出版年：2016
• 出版时间：March 2016
• 年：2016
• 卷：54
• 期：2-3
• 页码：453-461
• 全文大小：1,170 KB
• 参考文献：1.Abdel-Haleim A, Abdel-Razik Y, Ghalwash AZ, Sabry AA, Abdel-Rahman G (2008) Optic disc detection from normalized digital fundus images by means of a vessels’ direction matched filter. IEEE Trans Med Imaging 27:11–18CrossRef
  2.Akita K, Kuga H (1982) A computer method of understanding ocular fundus images. Pattern Recognit 15:431–443CrossRef
  3.Cassel GH, Billig MD, Randall HG (2001) The eye book: a complete guide to eye disorders and health. Johns Hopkins University Press, Baltimore
  4.Chaichana T, Yoowattana S, Sun Z, Tangjitkusolmun S, Sookpotharom S, Sangworasil M (2008) Edge detection of the optic disc in retinal images based on identification of a round shape. Communications and information technologies, international symposium, pp 670–674
  5.Cox MJ, Wood ICJ (1991) Computer-assisted optic nerve head assessment. Ophthalmic Physiol Opt 11:27–35CrossRef PubMed
  6.Fleming AD, Goatman KA, Philip S, Olson JA, Sharp PF (2007) Automatic detection of retinal anatomy to assist diabetic retinopathy screening. Phys Med Biol 52:331–345CrossRef PubMed
  7.Gonzales R, Woods C, Eddins RE (2004) Digital image processing. Prentice-Hall, Inc
  8.Hoover A, Goldbaum M (2003) Locating the optic nerve in retinal image using the fuzzy convergence of the blood vessels. IEEE Trans Med Imaging 22:951–958CrossRef PubMed
  9.Hoover A, Kouznetsova V, Goldbaum M (2000) Locating blood vessels in retinal images by piecewise threshold probing of a matched filter response. IEEE Trans Med Imaging 19(3):203–210CrossRef PubMed
  10.Hsiao H-K, Liu C-C, Yu C-Y, Kuo S-W, Yu S-S (2012) A novel optic disc detection scheme on retinal images. Expert Syst Appl 39(12):10600–10606CrossRef
  11.Kauppi T, Kalesnykiene V, Kamarainen J-K, Lensu L, Sorri I, Raninen A, Voutilainen R, Uusitalo H, Kälviäinen H, Pietilä J (2006) DIARETDB1 diabetic retinopathy database and evaluation protocol, Technical report
  12.Kong HJ, Kim SK, Seo JM, Park KH, Chung H, Park KS (2004) Three dimensional reconstruction of conventional stereo optic disc image. Annual international conference of the IEEE EMBS, Vol 12. San Francisco, pp 29–32
  13.Lalonde M, Beaulieu M, Gagnon L (2001) Fast and robust optic disc detection using pyramidal decomposition and Hausdorff-based template matching. IEEE Trans Med Imaging 20:1193–1200CrossRef PubMed
  14.Li H, Chutatape O (2001) Automatic location of optic disc in retinal images. IEEE ICIP, Thessaloniki, pp 837–840
  15.Lowell J, Hunter A, Steel D, Basu A, Ryder R, Fletcher E, Kennedy L (2004) Optic nerve head segmentation. IEEE Trans Med Imaging 23:256–264CrossRef PubMed
  16.Lupascu CA, Tegolo D, Rosa LD (2008) Automated detection of optic disc location in retinal images. 21st IEEE international symposium on computer-based medical systems, Finland, pp 17–22
  17.Morales S, Naranjo V, Perez D, Navea A, Alcaniz M (2012) Automatic detection of optic disc based on PCA and stochastic watershed. In: Signal processing conference (EUSIPCO), proceedings of the 20th European, Bucharest, pp 2605–2609
  18.Osareh A, Mirmehdi M, Thomas B, Markham, R (2002) Colour morphology and snakes for optic disc localization. The 6th medical image understanding and analysis conference, Vol 1, pp 21–24
  19.Patton N, Aslam TM, MacGillivray T, Deary IJ, Dhillon B, Eikelboom RH, Yogesan K, Constable IJ (2006) Retinal image analysis: concepts, applications and potential. Prog Retin Eye Res 25:99–127CrossRef PubMed
  20.Pereira C, Gonçalves L, Ferreira M (2013) Optic disc detection in color fundus images using ant colony optimization. Med Biol Eng Comput 51:295–303CrossRef PubMed
  21.Qureshi RJ, Kovacs L, Harangi B, Nagy B, Peto T, Hajdu A (2012) Combining algorithms for automatic detection of optic disc and macula in fundus images. Comput Vis Image Underst 116:138–145CrossRef
  22.Reza AW, Eswaran C, Hati S (2008) Automatic tracing of optic disc and exudates from color fundus images using fixed and variable thresholds. J Med Syst 33:73–80CrossRef
  23.Sekhar S, Al-Nuaimy W, Nandi AK (2008) Automated localization of retinal optic disc using Hough transform. The 5th IEEE international symposium on biomedical imaging: from nano to macro, Paris, pp 77–80
  24.Sinthanayothin C, Boyce JF, Cook HL, Williamson TH (1999) Automated localization of the optic disc, fovea, and retinal blood vessels from digital color fundus images. Br J Ophthalmol 83:902–910CrossRef PubMed PubMedCentral
  25.Sopharak A, Uyyanonvara B, Barman S, Williamson TH (2009) Automatic detection of diabetic retinopathy exudates from non-dilated retinal images using mathematical morphology methods. Comput Med Imaging Graph 32:720–727CrossRef
  26.Staal J, Abàmoff MD, Niemeijer M, Viergever MA, van Ginneken B (2004) Ridgebased vessel segmentation in color images of the retina. IEEE Trans Med Imaging 23(4):501–509CrossRef PubMed
  27.Tobin KW, Chaum E, Govindasamy VP, Karnowski T (2007) Detection of anatomic structures in human retinal imagery. IEEE Trans Med Imaging 26:1729–1739CrossRef PubMed
  28.Walter T, Klein JC, Massin P, Erginary A (2002) A contribution of image processing to the diagnosis of diabetic retinopathy-detection of exudates in color fundus images of human retina. IEEE Trans Med Imaging 21:1236–1243CrossRef PubMed
  29.Welfer D, Scharcanski J, Kitamura C, Pizzol MD, Ludwig L, Marinho D (2010) Segmentation of the optic disc in color eye fundus images using an adaptive morphological approach. Comput Biol Med 40:124–137CrossRef PubMed
  30.Yang XS (2008) Nature-inspired metaheuristic algorithms. Luniver Press, Frome
  31.Yavuz Z, İkibaş C, Şevik U, Köse C (2009) A method for automatic optic disc extraction in retinal fundus images. 5th International advanced technologies symposium, Karabuk, pp 93–98
  
• 作者单位：Javad Rahebi (1)
  Fırat Hardalaç (1)
  
  1. Department of Electrical and Electronics Engineering, Gazi University, Ankara, Turkey
  
• 刊物类别：Engineering
• 刊物主题：Biomedical Engineering
  Human Physiology
  Imaging and Radiology
  Computer Applications
  Neurosciences
  
• 出版者：Springer Berlin / Heidelberg
• ISSN：1741-0444

文摘

There are various methods and algorithms to detect the optic discs in retinal images. In recent years, much attention has been given to the utilization of the intelligent algorithms. In this paper, we present a new automated method of optic disc detection in human retinal images using the firefly algorithm. The firefly intelligent algorithm is an emerging intelligent algorithm that was inspired by the social behavior of fireflies. The population in this algorithm includes the fireflies, each of which has a specific rate of lighting or fitness. In this method, the insects are compared two by two, and the less attractive insects can be observed to move toward the more attractive insects. Finally, one of the insects is selected as the most attractive, and this insect presents the optimum response to the problem in question. Here, we used the light intensity of the pixels of the retinal image pixels instead of firefly lightings. The movement of these insects due to local fluctuations produces different light intensity values in the images. Because the optic disc is the brightest area in the retinal images, all of the insects move toward brightest area and thus specify the location of the optic disc in the image. The results of implementation show that proposed algorithm could acquire an accuracy rate of 100 % in DRIVE dataset, 95 % in STARE dataset, and 94.38 % in DiaRetDB1 dataset. The results of implementation reveal high capability and accuracy of proposed algorithm in the detection of the optic disc from retinal images. Also, recorded required time for the detection of the optic disc in these images is 2.13 s for DRIVE dataset, 2.81 s for STARE dataset, and 3.52 s for DiaRetDB1 dataset accordingly. These time values are average value.