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Robust intensity variation and inverse surface adaptive thresholding techniques for detection of optic disc and exudates in retinal fundus images

Identyfikatory
Warianty tytułu
Języki publikacji
EN
Abstrakty
EN
Diabetic Retinopathy (DR) is an adverse change in retinal blood vessels leads to blindness for diabetic patients without any symptoms. Diabetes is characterized by imbalance level of glucose in the human body. The optic disc (OD) is the major retinal landmark. Localization of OD is an important step in fundus image analysis and to develop Computer Aided Diagnosis tool for DR. OD center detection is necessary to reduce false positive rate in the detection of exudates (EXs). EXs is the white lesion present in the retina which is the early symptom for the diagnosis of DR. OD is detected using intensity variation algorithm and EXs is segmented using inverse surface adaptive thresholding algorithm. This algorithm achieves better result in localizing OD and segmenting EXs when compared to literature-reviewed methods. The maximum intensity variance method is used to locate OD with average ACC of 96.54%, 98.65%, 98.12%, 99.23%, 99.81% and 98.47% in DIARETDB0, DIARETDB1, MESSIDOR, DRIVE, STARE and Bejan Singh Eye Hospital databases with less computation time of 102 ms, 108 ms, 120 ms, 93 ms, 110 ms and 131 ms. The inverse surface adaptive thresholding method has achieved an SE of 97.43%, 98.87%, 99.12%, 97.21%, 98.72%, and 96.63%, a SPE of 91.56%, 92.31%, 90.21%, 90.14%, 89.58%, 92.56% and an ACC of 99.34%, 99.67%, 98.34%, 98.87%, 99.13%, 98.34% for DIARETDB0, DIARETDB1, MESSIDOR, DRIVE, STARE and Bejan Singh Eye Hospital databases respectively.
Twórcy
  • Department of Computer Science and Engineering, Kalasalingam Academy of Research and Education, Sriviliputtur, Tamilnadu, India
  • Department of Information Technology, National Engineering College, Kovilpatti, Tamilnadu, India
Bibliografia
  • [1] Mo J, Zhang L, Feng Y. Exudate-based diabetic macular edema recognition in retinal images using cascaded deep residual networks. Neurocomputing 2018;290:161–71.
  • [2] Mahendran G, Dhanasekaran R. Investigation of the severity level of diabetic retinopathy using supervised classifier algorithms. Comput Electr Eng 2015;45:312–23.
  • [3] Xiong L, Li H. An approach to locate optic disc in retinal images with pathological changes. Comput Med Imaging Graph 2016;47:40–50.
  • [4] Soares I, Castelo-Branco M, Pinheiro AMG. Optic disc localization in retinal images based on cumulative sum fields. IEEE J Biomed Health Inform 2016;20(2):574–85.
  • [5] Zhang D, Zhao Y. Novel accurate and fast optic disc detection in retinal images with vessel distribution and directional characteristics. IEEE J Biomed Health Inform 2016;20(1):333–42.
  • [6] Harangi B, Hajdu A. Detection of the optic disc in fundus images by combining probability models. Comput Biol Med 2015;65:10–24.
  • [7] Marin D, Gegundez-Arias ME, Suero A, Bravo JM. Obtaining optic disc center and pixel region by automatic thresholding methods on morphologically processed fundus images. Comput Methods Programs Biomed 2015;118:173–85.
  • [8] Imani E, Pourreza HR. A novel method for retinal exudate segmentation using signal separation algorithm. Comput Methods Programs Biomed 2016;133:195–205.
  • [9] Pereira C, Gonçalves L, Ferreira M. Exudate segmentation in fundus images using an ant colony optimization approach. Inform Sci 2015;296:14–24.
  • [10] Agurto C, Murray V, Yu H, Wigdahl J, Pattichis M, Nemeth S, Barriga ES, Soliz P. A multiscale optimization approach to detect exudates in the macula. IEEE J Biomed Health Inform 2014;18(4):1328–36.
  • [11] Ali S, Sidibé D, Adal KM, Giancardo L, Chaum E, Karnowski TP, Mériaudeau F. Statistical atlas based exudate segmentation. Comput Med Imaging Graph 2013;37(5):358–68.
  • [12] Annunziata R, Garzelli A, Ballerini L, Mecocci A, Trucco E. Leveraging multiscale Hessian-based enhancement with a novel exudate inpainting technique for retinal vessel segmentation. IEEE J Biomed Health Inform 2016;20 (4):1129–38.
  • [13] Naqvi SAG, Zafar MF, ul Haq I. Referral system for hard exudates in eye fundus. Comput Biol Med 2015;64:217–35.
  • [14] Zhang X, Thibault G, Decencière E, Marcotegui B, Laÿ B, Danno R, Cazuguel G, Quellec G, Lamard M, Massin P, Chabouis A. Exudate detection in color retinal images for mass screening of diabetic retinopathy. Med Image Anal 2014;18(7):1026–43.
  • [15] Harangi B, Hajdu A. Automatic exudate detection by fusing multiple active contours and regionwise classification. Comput Biol Med 2014;54:156–71.
  • [16] Almazroa A, Burman R, Raahemifar K, Lakshminarayanan V. Optic disc and optic cup segmentation methodologies for glaucoma image detection: a survey. J Ophthalmol 2015;2015.
  • [17] Zou B, Chen C, Zhu C, Duan X, Chen Z. Classified optic disc localization algorithm based on verification model. Comput Graph 2018;70:281–7.
  • [18] Kaur J, Mittal D. Estimation of severity level of non-proliferative diabetic retinopathy for clinical aid. Biocybern Biomed Eng 2018;38(3):708–32.
  • [19] Panda R, Puhan NB, Panda G. Robust and accurate optic disk localization using vessel symmetry line measure in fundus images. Biocybern Biomed Eng 2017;37(3):466–76.
  • [20] Bharkad S. Automatic segmentation of optic disk in retinal images. Biomed Signal Process Control 2017;31:483–98.
  • [21] Dai B, Wu X, Bu W. Optic disc segmentation based on variational model with multiple energies. Pattern Recognit 2017;64:226–35.
  • [22] Rodrigues LC, Marengoni M. Segmentation of optic disc and blood vessels in retinal images using wavelets, mathematical morphology and Hessian-based multi-scale filtering. Biomed Signal Process Control 2017;36:39–49.
  • [23] Díaz-Pernil D, Fondón I, Peña-Cantillana F, Gutiérrez- Naranjo MA. Fully automatized parallel segmentation of the optic disc in retinal fundus images. Pattern Recognit Lett 2016;83:99–107.
  • [24] Panda R, Puhan NB, Panda G. Global vessel symmetry for optic disc detection in retinal images. Computer vision, pattern recognition, image processing and graphics (NCVPRIPG), Fifth National Conference on (pp. 1–4). IEEE; 2015 December.
  • [25] Almazroa A, Burman R, Raahemifar K, Lakshminarayanan V. Optic disc and optic cup segmentation methodologies for glaucoma image detection: a survey. J Ophthalmol 2015. http://dx.doi.org/10.1155/2015/180972. Article ID 180972, 28 pages.
  • [26] Giachetti A, Ballerini L, Trucco E. Accurate and reliable segmentation of the optic disc in digital fundus images. J Med Imaging 2014;1(2):024001. http://dx.doi.org/10.1117/1.JMI.1.2.024001. 1–12.
  • [27] Ravi T, Aditya VVS, Rani BMS, Boppana M. Segmentation of blood vessels and optic disc in retinal images. J Telemat Inform 2015;3(1):1–6.
  • [28] Sundaresan V, Ram K, Joshi N, Sivaprakasam M, Gandhi R. Integrated approach for accurate localization of optic disc and macula. Proceedings of the ophthalmic medical image analysis first international workshop, OMIA 2014, held in conjunction with MICCAI 2014, Boston, Massachusetts, September 14; 2014. p. 97–104. http://dx.doi.org/10.17077/omia.1014.
  • [29] Foracchia M, Grisan E, Ruggeri A. Detection of optic disc in retinal images by means of a geometrical model of vessel structure. IEEE Trans Med Imaging 2004;23(10):1189–95.
  • [30] Mendonca AM, Sousa A, Mendonça L, Campilho A. Automatic localization of the optic disc by combining vascular and intensity information. Comput Med Imaging Graph 2013;37(5–6):409–17.
  • [31] Gui B, Shuai RJ, Chen P. Optic disc localization algorithm based on improved corner detection. Procedia Comp Sci 2018;131:311–9.
  • [32] Ünver HM, Kökver Y, Duman E, Erdem OA. Statistical edge detection and circular Hough transform for optic disk localization. Appl Sci 2019;9(2):350.
  • [33] Hou K, Liu N, Jia W, He Y, Lian J, Zheng Y. Optic disc detection from fundus photography via best-buddies similarity. Appl Sci 2018;8(5):709.
  • [34] Muhammed LA. Localizing optic disc in retinal image automatically with entropy based algorithm. Int J Biomed Imaging 2018;2018.
  • [35] Pruthi J, Arora S, Khanna K. Metaheuristic techniques for detection of optic disc in retinal fundus images. 3D Research 2018;9(4):47.
  • [36] Reza MN. Automatic detection of optic disc in color fundus retinal images using circle operator. Biomed Signal Process Control 2018;45:274–83.
  • [37] Hoover AD, Kouznetsova V, Goldbaum M. Locating blood vessels in retinal images by piecewise threshold probing of a matched filter response. IEEE Trans Med Imaging 2000;19 (3):203–10.
  • [38] Yazid H, Arof H, Isa HM. Exudates segmentation using inverse surface adaptive thresholding. Measurement 2012;45(6):1599–608.
Uwagi
PL
Opracowanie rekordu w ramach umowy 509/P-DUN/2018 ze środków MNiSW przeznaczonych na działalność upowszechniającą naukę (2019).
Typ dokumentu
Bibliografia
Identyfikator YADDA
bwmeta1.element.baztech-53a0c835-cc3e-4124-9540-f92ab080f232
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