Abstract
Optic disk (OD) detection and recognition is an important stage for developing automatic screening applications of diabetic retinopathy disease in color retinal image. However, the retinal image has a low resolution and was influenced by salt-and-paper noise. Therefore, a retinal image needs a preprocessing procedure (i.e., color image normalization, image enhancement and noise removal) prior to the use of the retinal images. Afterward, a combination of a maker-controlled watershed segmentation and mathematical morphology exiting that was applied to detect of OD. These two methods have complementary drawbacks and advantages, and this is the motivation for the hybrid method presented. These modifications enable the proposed methods to become more robust and accurate to detection of the OD regions. The methods were evaluated by applying to two-color retinal dataset [local dataset in Thailand and a public available diabetic retinopathy database (STARE)]. Although the retinal images in this paper are fairly low, the results showed the proposed method has the performance of the OD detection about 99.33% on 300 images from the local dataset and 95.06% of 81 images from the STARE dataset, taking an average computational time of 3.4 s per image. These results show effectiveness in both detections of the OD regions and boundary.
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References
Abramoff MD, Alward WLM, Greenlee EC, Shuba L, Kim CY, Finger JH, Kwon YH (2007) Automatic segmentation of the optic disc from stereo color photographs using physiologically plausible features. Invest Ophthalmol Vis Sci 48:1665–1673
Aquino A, Gegundez-Arias ME, Marin D (2010) Detecting the optic disc boundary in digital retinal images using morphological, edge, detection, and feature extraction techniques. Trans Med Imaging 29:1860–1869
Barrett SF, Naess E, Molvik T (2001) Employing the Hough transform to locate the optic disc. Biomed Sci Instrum 37:81–86
Chrastek R, Wolf M, Donath K, Niemann H, Paulus D, Hothorn T, Lausen B, Lammer R, Mardin CY, Michelson G (2005) Automated segmentation of the optic nerve head for diagnosis of glaucoma. Med Imaging Anal 9:297–314
Digabel H, Lantuejoul C (1997) Iterative algorithms. In: Proceeding of the \(2^{\rm nd}\) European symposium on quantitative analysis of microstructures in materials science, biology and medicine, pp 85–99
Eswaran C, Reza A, Hati S (2008) Extraction of the contours of optic disc and exudates based on marker-controlled watershed segmentation. In: International conference on computer science and information technology, pp 719–723
Haar FT (2005) Automatic localization of the optic disc in digital color images of the human retina. M.S. Thesis, Utrecht University, Utrecht, The Netherlands
Hajar J, Kamel H, Noureddine E (2008) Localization of the optic disc in retinal image using watersnake. In: Proceeding international conference computer and communication engineering, pp 947–951
Hoover A, Goldbaum M (2003) Locating the optic disc in a retinal image using the fuzzy convergence of the blood vessels. IEEE Trans Med Imaging 22:951–958
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 20:1193–1200
Li H, Chutatape O (2001) Automatic localization of optic disc in retinal images. IEEE Int Conf Image Process 2:837–840
Li H, Chutatape O (2003) A model-based approach for automated feature extraction in retinal images. In: 19th Conference on computer vision, vol 1, pp 394–399
Lowell J, Hunter A, Steel D, Basu A, Ryder R, Fletcher E, Kennedy L (2004) Optic nerve head segmentation. IEEE Trans Med Imaging 23:256–264
Osareh A, Mirmehdi M, Thomas B, Markham R (2002a) Classification and localization of diabetic related eye disease. In: 16th European conference on computer vision, vol 2353, pp 502–516
Osareh A, Mirmehdi M, Thomas B, Markham R (2002b) Comparison of color spaces for optic disc localization in retinal images. In: Proceedings 16th IEEE international conference on pattern recognition, vol 1, pp 743–746
Otsu N (1978) A threshold selection method from grayscale histogram. IEEE Trans Syst Man Cybern 8:62–66
Park M, Jin JS, Luo S (2006) Locating the optic disc in retinal images. In: Proceeding IEEE international conference on computer graphics, imaging and visualization, pp 141–145
Qureshi RJ, Kovacs L, Harangi B, Nagy B, Peto T, Hajdu A (2012) Combining algorithms for automatic detection of optic disc and macula in retinal images. Comput Vis Image Underst 116:138–145
Reza AW, Eswaran C, Hati S (2008) Automatic tracing of optic disc and exudates from color retinal images using fixed and variable threshold. J Med Syst 33:73–80
Sinthanayothin C, Boyce JF, Cook HL, Williamson TH (1999) Automated localization of the optic disc, fovea, and retinal blood vessels from digital color retinal images. Br J Ophthalmol 83:902–910
Sinthanayothin C, Boyce JF, Williamson TH, Cook HL, Mensah E, Lal S, Usher D (2002) Automated detection of diabetic retinopathy on digital fundus images. Diabet Med 19(2):105–112
STARE project website Clemson University, Clemson, SC. http://www.ces.clemson.edu/~ahoover/stare
Tobin KW, Chaum E, Govindasamy VP, Karnowski TP, Sezer O (2006) Characterization of the optic in retinal imagery using a probabilistic approach. In: Joseph M, Josien PW (eds) Medical imaging image processing, vol 6144, pp 1088–1097
Vincent L, Soille P (1991) Watershed in digital spaces: an efficient algorithm based on immersion simulation. IEEE Trans Pattern Anal Mach Intell 13:583–598
Walter T, Klein JC, Massin P, Erginay A (2002) A contribution of exudates in color retinal images of the human retina. IEEE Trans Med Imaging 21:1236–1243
Walter T, Klein JC (2001) Segmentation of color retinal images of the human retina: detection of the optic disc and the vascular tree using morphological techniques. In: Proceeding 2nd international symposium on medical data analysis, pp 282–287
Welfer D, Schareanski J, Kitamura CM, Pizzol MMD, Ludwig LW, Marinho DR (2010) Segmentation of the optic disc in color eye retinal images using an adaptive morphological approach. Comput Biol Med 40:124–137
Wong DWK, Liu J, Lim JH, Jia X, Yin F, Li H, Wong TY (2008) Level set based automatic cup-to-disc ratio determination using retina retinal images in ARGALI. In: Annual international conference of the IEEE engineering in medicine and biology society, pp 2266–2269
Xu J, Chutatape O, Sung E, Zheng C, Kuan PCT (2007) Optic disc feature extraction via modified deformable model technique for glaucoma analysis. Patt Recogn 40:2063–2076
Youssif AAHAR, Ghalwash AZ, Ghoneim AR (2008) Optic disc detection from normalized digital retinal images by means of vessels direction matched filter. IEEE Trans Med Imaging 27:11–18
Acknowledgements
The authors would like to thank the local datasets in Thailand used in this work. This research work is supported by Mahasarakham University, Thailand. The authors wish to thank M.D. Ekkarat Pothiruk, Khon Kaen Hospital, Thailand, for having kindly provided the exudates detection for this study.
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Wisaeng, K., Sa-ngiamvibool, W. Automatic detection and recognition of optic disk with maker-controlled watershed segmentation and mathematical morphology in color retinal images. Soft Comput 22, 6329–6339 (2018). https://doi.org/10.1007/s00500-017-2681-9
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DOI: https://doi.org/10.1007/s00500-017-2681-9