Abstract
In this work, we present a new methodology to simultaneously segment anatomical structures in medical images. Additionally, this methodology is instantiated in a method that is used to simultaneously segment the optic disc (OD) and fovea in retinal images. The OD and fovea are important anatomical structures that must be previously identified in any image-based computer-aided diagnosis system dedicated to diagnosing retinal pathologies that cause vision problems. Basically, the simultaneous segmentation method uses an OD-fovea model and an evolutionary algorithm. On the one hand, the model is built using the intra-structure relational knowledge, associated with each structure, and the inter-structure relational knowledge existing between both and other retinal structures. On the other hand, the evolutionary algorithm (differential evolution) allows us to automatically adjust the instance parameters that best approximate the OD-fovea model in a given retinal image. The method is evaluated in the MESSIDOR public database. Compared with other recent segmentation methods in the related literature, competitive segmentation results are achieved. In particular, a sensitivity and specificity of 0.9072 and 0.9995 are respectively obtained for the OD. Considering a success when the distance between the detected and actual center is less than or equal to \(\eta\) times the OD radius, the success rates obtained for the fovea are 97.3% and 99.0% for \(\eta =1/2\) and \(\eta =1\), respectively. The segmentation average time per image is 29.35 s.
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References
Hubbard LD, Brothers RJ, King WN, Clegg LX, Klein R, Cooper LS, Sharrett AR, Davis MD, Cai J (1999) Methods for evaluation of retinal microvascular abnormalities associated with hypertension/sclerosis in the atherosclerosis risk in communities study. Ophthalmology 106(12):2269–2280
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(1):99–127
Youssif AA-HA-R, Ghalwash AZ, Ghoneim AASA-R (2008) Optic disc detection from normalized digital fundus images by means of a vessels’ direction matched filter. IEEE Trans Med Imaging 27(1):11–18
Winder RJ, Morrow PJ, McRitchie IN, Bailie JR, Hart PM (2009) Algorithms for digital image processing in diabetic retinopathy. Comput Med Imaging Gr 33(8):608–622
Welfer D, Scharcanski J, Marinho DR (2011) Fovea center detection based on the retina anatomy and mathematical morphology. Comput Methods Progr Biomed 104(3):397–409
Medhi JP, Dandapat S (2016) An effective fovea detection and automatic assessment of diabetic maculopathy in color fundus images. Comput Biol Med 74:30–44
Molina-Casado JM, Carmona EJ, García-Feijoó J (2017) Fast detection of the main anatomical structures in digital retinal images based on intra- and inter-structure relational knowledge. Comput Methods Progr Biomed 149:55–68
Aquino A, Gegúndez-Arias ME, Marin D (2010) Detecting the optic disc boundary in digital fundus images using morphological, edge detection, and feature extraction techniques. IEEE Trans Med Imaging 29(11):1860–1869
Welfer D, Scharcanski J, Kitamura CM, Dal Pizzol MM, Ludwig LWB, Marinho RD (2010) Segmentation of the optic disk in color eye fundus images using an adaptive morphological approach. Comput Biol Med 40(2):124–137
Morales S, Naranjo V, Angulo J, Alcañiz ML (2013) Automatic detection of optic disc based on PCA and mathematical morphology. IEEE Trans Med Imaging 32(4):786–796
Zhu X, Rangayyan RM, Ells AL (2010) Detection of the optic nerve head in fundus images of the retina using the hough transform for circles. J Digit Imaging 23(3):332–341
Carmona EJ, Rincón M, García-Feijoo J, Martínez-de-la Casa JM (2008) Identification of the optic nerve head with genetic algorithms. Artif Intell Med 43:243–259
Novo J, Penedo MG, Santos J (2009) Localisation of the optic disc by means of GA-optimised topological active nets. Image Vis Comput 27(10):1572–1584
Molina JM, Carmona EJ (2011) Localization and segmentation of the optic nerve head in eye fundus images using pyramid representation and genetic algorithms. In: Ferrández JM et al (eds) Foundations on natural and artificial computation (part I). Springer, Berlin, pp 431–440
Arnay R, Fumero F, Sigut J (2017) Ant colony optimization-based method for optic cup segmentation in retinal images. Appl Soft Comput 52:409–417
Lowell J, Hunter A, Steel D, Basu A, Ryder R, Fletcher E (2004) Optic nerve head segmentation. IEEE Trans Med Imaging 23(2):256–264
Giachetti A, Ballerini L, Trucco E (2014) Accurate and reliable segmentation of the optic disc in digital fundus images. J Med Imaging 1(2):024001–024001
Dashtbozorg B, Mendonça AM, Campilho A (2015) Optic disc segmentation using the sliding band filter. Comput Biol Med 56:1–12
Yu H, Barriga ES, Agurto C, Echegaray S, Pattichis MS, Bauman W, Soliz P (2012) Fast localization and segmentation of optic disk in retinal images using directional matched filtering and level sets. IEEE Trans Inf Technol Biomed 16(4):644–657
Cheng J, Liu J, Xu Y, Yin F, Wong DWK, Tan N-M, Tao D, Cheng C-Y, Aung T, Wong TY (2013) Superpixel classification based optic disc and optic cup segmentation for glaucoma screening. IEEE Trans Med Imaging 32(6):1019–1032
Singh J, Joshi GD, Sivaswamy J (2008) Appearance-based object detection in colour retinal images. In: 15th IEEE international conference on image processing, pp 1432–1435. IEEE
Salazar-Gonzalez A, Kaba D, Li Y, Liu X (2014) Segmentation of the blood vessels and optic disk in retinal images. IEEE J Biomed Health Inform 18(6):1874–1886
Marin D, Gegundez-Arias ME, Suero A, Bravo JM (2015) Obtaining optic disc center and pixel region by automatic thresholding methods on morphologically processed fundus images. Comput Methods Progr Biomed 118(2):173–185
Yu H, Barriga ES, Agurto C, Echegaray S, Pattichis M, Zamora G, Bauman W, Soliz P (2011) Fast localization of optic disc and fovea in retinal images for eye disease screening. SPIE Med Imaging 7963:796317–796329
Gegundez ME, Marin D, Bravo JM, Suero A (2013) Locating the fovea center position in digital fundus images using thresholding and feature extraction techniques. Comput Med Imaging Graph 37(5):386–393
Kao EF, Lin P-C, Chou M-C, Jaw TS, Liu GC (2014) Automated detection of fovea in fundus images based on vessel-free zone and adaptive gaussian template. Comput Methods Progr Biomed 117(2):92–103
Aquino A (2014) Establishing the macular grading grid by means of fovea centre detection using anatomical-based and visual-based features. Comput Biol Med 55:61–73
Chin KS, Trucco E, Tan L, Wilson PJ (2013) Automatic fovea location in retinal images using anatomical priors and vessel density. Pattern Recognit Lett 34(10):1152–1158
Girard F, Kavalec C, Grenier S, Tahar HB, Cheriet F (2016) Simultaneous macula detection and optic disc boundary segmentation in retinal fundus images. In: SPIE medical imaging, vol 9784, pp 97841F1–97841F9
Tan JH, Acharya UR, Bhandary SV, Chua KC, Sivaprasad S (2017) Segmentation of optic disc, fovea and retinal vasculature using a single convolutional neural network. J Comput Sci 20:70–79
MESSIDOR. Messidor database (2012). http://www.adcis.net/en/third-party/messidor/. Accessed 22 Mar 2019
Decenciere E, Zhang X, Cazuguel G, Lay B, Cochener B, Trone C, Gain P, Ordonez R, Massin P, Erginay A, Charton B, Klein JC (2014) Feedback on a publicly distributed database: the messidor database. Image Anal Stereol 33(3):231–234
ONHSD. Optic nerve head segmentation dataset (2013). http://www.aldiri.info/Image%20Datasets/ONHSD.aspx. Accessed 22 March 2019
DIARETDB1. The diabetic retinopathy database (2015). http://www.it.lut.fi/project/imageret/diaretdb1/. Accessed 22 March 2019
Kauppi T, Kalesnykiene V, Kamarainen JK, Lensu L, Sorri I, Raninen A, Voutilainen R, Uusitalo H, Kälviäinen H, Pietilä J (2007) The diaretdb1 diabetic retinopathy database and evaluation protocol. In: Proceedings of the 11th conference on medical image understanding and analysis, pp 61–65
UniHuelva. Messidor fovea annotations (2013). http://www.uhu.es/retinopathy/muestras/Provided_Information.zip. Accessed 22 March 2019
Atkinson A Mazo C (2011) Imaged area of the retina. https://www.freelists.org/archives/optimal/02-2017/pdf91WmMGLh6Q.pdf. Accessed 22 March 2019
Lee S, Abramoff MD, Reinhardt JM (2010) Retinal atlas statistics from color fundus images. SPIE Med Imaging 7623:762310–762319
Pallawala PMDS, Hsu W, Lee ML, Eong K-GA (2004) Automated optic disc localization and contour detection using ellipse fitting and wavelet transform. In: Pajdla Tomás, Matas Jiří (eds) 8th European conference on computer vision, Springer, Berlin, pp 139–151
Cheng J, Liu J, Wong DWK, Yin F, Cheung C, Baskaran M, Aung T, Wong TY (2011) Automatic optic disc segmentation with peripapillary atrophy elimination. In Annual international conference of the IEEE engineering in medicine and biology society. IEEE, pp 6224–6227
Roychowdhury S, Koozekanani DD, Kuchinka SN, Parhi KK (2016) Optic disc boundary and vessel origin segmentation of fundus images. IEEE J Biomed Health Inform 20(6):1562–1574
Raja JB, Ravichandran CG (2014) Automatic localization of fovea in retinal images based on mathematical morphology and anatomic structures. Int J Eng Technol 6(5):2171–2183
Schwiegerling J (2004) Field guide to visual and ophthalmic optics. SPIE Press, Bellingham
Xu X (2010) Simultaneous automatic detection of optic disc and fovea. Master’s thesis, University of Iowa
Sinthanayothin C, Boyce JF, Cook HL, Williamson TH (1999) Automated localisation of the optic disc, fovea, and retinal blood vessels from digital colour fundus images. Br J Ophthalmol 83(8):902–910
Jonas RA, Wang YX, Yang H, Li JJ, Xu L, Panda-Jonas S, Jonas JB (2015) Optic disc—fovea angle: the Beijing eye study 2011. PLOS ONE 10(11):1–10, 11
Storn R, Price K (1997) Differential evolution—a simple and efficient heuristic for global optimization over continuous spaces. J Glob Optim 11(4):341–359
Das S, Suganthan PN (2011) Differential evolution: a survey of the state-of-the-art. IEEE Trans Evolut Comput 15(1):4–31
Ugolotti R, Nashed YSG, Mesejo P, Ivekovic S, Mussi L, Cagnoni S (2013) Particle swarm optimization and differential evolution for model-based object detection. Appl Soft Comput 13(6):3092–3105
Mesejo P, Ugolotti R, Di Cunto F, Giacobini M, Cagnoni S (2013) Automatic hippocampus localization in histological images using differential evolution-based deformable models. Pattern Recognit Lett 34(3):299–307
Saraswat M, Arya KV, Sharma H (2013) Leukocyte segmentation in tissue images using differential evolution algorithm. Swarm Evolut Comput 11:46–54
Mesejo P, Ibañez O, Cordón O, Cagnoni S (2016) A survey on image segmentation using metaheuristic-based deformable models: state of the art and critical analysis. Appl Soft Comput 44:1–29
Holland JH (1992) Adaptation in natural and artificial systems: an introductory analysis with applications to biology. control and artificial intelligence. MIT Press, Cambridge
Beyer H-G, Schwefel H-P (2002) Evolution strategies—a comprehensive introduction. Nat Comput 1:3–52
Hansen N, Ostermeier A (2001) Completely derandomized self-adaptation in evolution strategies. Evolut Comput 9(2):159–195
Kennedy J, Eberhart RC (2001) Swarm intelligence. Morgan Kaufmann Publishers Inc., San Francisco
Wang S, Zhang Y, Ji G, Yang J, Jianguo W, Wei L (2015) Fruit classification by wavelet-entropy and feedforward neural network trained by fitness-scaled chaotic abc and biogeography-based optimization. Entropy 17:5711–5728
Wang S, Li P, Chen P, Phillips P, Liu G, Sidan D, Zhang Y (2017) Pathological brain detection via wavelet packet tsallis entropy and real-coded biogeography-based optimization. Fund Inform 151:275–291
Price K, Storn RM, Lampinen JA (2005) Differential evolution: a practical approach to global optimization (natural computing series). Springer, New York
Rehman ZU, Naqvi SS, Khan TM, Arsalan M, Khan MA, Khalil MA (2019) Multi-parametric optic disc segmentation using superpixel based feature classification. Expert Syst Appl 120:461–473
GeethaRamani R, Balasubramanian L (2018) Macula segmentation and fovea localization employing image processing and heuristic based clustering for automated retinal screening. Comput Methods Progr Biomed 160:153–163
Wang L, Liu H, Yaling L, Chen H, Zhang J, Jiantao P (2019) A coarse-to-fine deep learning framework for optic disc segmentation in fundus images. Biomed Signal Process Control 51:82–89
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(1):138–145
Acknowledgements
The authors would like to thank the ONHSD and MESSIDOR program partners for facilitating their respective databases. We would also like to express our gratitude to Gegundez-Arias et al. [25] for allowing us to access their MESSIDOR fovea ground truth.
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Carmona, E.J., Molina-Casado, J.M. Simultaneous segmentation of the optic disc and fovea in retinal images using evolutionary algorithms. Neural Comput & Applic 33, 1903–1921 (2021). https://doi.org/10.1007/s00521-020-05060-w
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DOI: https://doi.org/10.1007/s00521-020-05060-w