Automated detection of optic disc contours in fundus images using decision tree classifier

Biocybernetics and Biomedical Engineering - Tập 40 - Trang 52-64 - 2020
Sumaiya Pathan1, Preetham Kumar1, Radhika Pai1, Sulatha V. Bhandary2
1Department of Information and Communication Technology, Manipal Institute of Technology, Manipal Academy of Higher Education, Manipal, Karnataka, India
2Dept. of Ophthalmology, Kasturba Medical College (KMC), Manipal Academy of Higher Education, Manipal, Karnataka, India

Tài liệu tham khảo

Indiana retina. Available from https://www.indianaretina.com/diseases-of-the-eye. Singh, 2016, Segmentation of retinal blood vessels by using a matched filter based on second derivative of Gaussian, Int J Biomed Eng Technol, 21, 229, 10.1504/IJBET.2016.078286 Morales, 2013, Automatic detection of optic disc based on PCA and mathematical morphology, IEEE Trans Med Imaging, 32, 786, 10.1109/TMI.2013.2238244 Cheng, 2003, A novel approach to diagnose diabetes based on the fractal characteristics of retinal images, IEEE Trans Inf Technol Biomed, 7, 163, 10.1109/TITB.2003.813792 Rodrigues, 2017, Segmentation of optic disc and blood vessels in retinal images using wavelets, mathematical morphology and Hessian-based multi-scale filtering, Biomed Signal Process Control, 36, 39, 10.1016/j.bspc.2017.03.014 Bharkad, 2017, Automatic segmentation of optic disk in retinal images, Biomed Signal Process Control, 31, 483, 10.1016/j.bspc.2016.09.009 Nayak, 2008, Automated diagnosis of Glaucoma Using digital fundus images, J Med Syst, 33, 337 Issac, 2015, An adaptive threshold based image processing technique for improved glaucoma detection and classification, Comput Methods Programs Biomed, 122, 229, 10.1016/j.cmpb.2015.08.002 Aquino, 2010, Detecting the optic disc boundary in digital fundus images using morphological, edge detection, and feature extraction techniques, IEEE Trans Med Imaging, 29, 1860, 10.1109/TMI.2010.2053042 Welfer, 2010, Segmentation of the optic disk in color eye fundus images using an adaptive morphological approach, Comput Biol Med, 40, 124, 10.1016/j.compbiomed.2009.11.009 Muramatsu, 2011, Automated segmentation of optic disc region on retinal fundus photographs: comparison of contour modeling and pixel classification methods, Comput Methods Programs Biomed, 101, 23, 10.1016/j.cmpb.2010.04.006 Mittapalli, 2016, Segmentation of optic disk and optic cup from digital fundus images for the assessment of glaucoma, Biomed Signal Process Control, 24, 34, 10.1016/j.bspc.2015.09.003 Mookiah, 2012, Automated detection of optic disk in retinal fundus images using intuitionistic fuzzy histon segmentation, Proc Inst Mech Eng H, 227, 37, 10.1177/0954411912458740 Joshi, 2011, Optic disk and cup segmentation from monocular color retinal images for Glaucoma assessment, IEEE Trans Med Imaging, 30, 1192, 10.1109/TMI.2011.2106509 Yu, 2012, Fast localization and segmentation of optic disk in retinal images using directional matched filtering and level sets, IEEE Trans Inf Technol Biomed, 16, 644, 10.1109/TITB.2012.2198668 Zilly, 2017, Glaucoma detection using entropy sampling and ensemble learning for automatic optic cup and disc segmentation, Comput Med Imaging Graph, 55, 28, 10.1016/j.compmedimag.2016.07.012 Al-Bander, 2018, Multiscale sequential convolutional neural networks for simultaneous detection of fovea and optic disc, Biomed Signal Process Control, 40, 91, 10.1016/j.bspc.2017.09.008 Zhang, 2016, Novel accurate and fast optic disc detection in retinal images with vessel distribution and directional characteristics, IEEE J Biomed Health Inform, 20, 333, 10.1109/JBHI.2014.2365514 Li, 2018, Learning supervised descent directions for optic disc segmentation, Neurocomputing, 275, 350, 10.1016/j.neucom.2017.08.033 Sarathi, 2016, Blood vessel inpainting based technique for efficient localization and segmentation of optic disc in digital fundus images, Biomed Signal Process Control, 25, 108, 10.1016/j.bspc.2015.10.012 Xiong, 2016, An approach to locate optic disc in retinal images with pathological changes, Comput Med Imaging Graph, 47, 40, 10.1016/j.compmedimag.2015.10.003 Mendonça, 2013, Automatic localization of the optic disc by combining vascular and intensity information, Comput Med Imaging Graph, 37, 409, 10.1016/j.compmedimag.2013.04.004 Zou, 2018, Classified optic disc localization algorithm based on verification model, Comput Graph, 70, 281, 10.1016/j.cag.2017.07.031 Roychowdhury, 2016, Optic disc boundary and vessel origin segmentation of fundus images, IEEE J Biomed Health Inform, 20, 1562, 10.1109/JBHI.2015.2473159 Miri, 2015, Multimodal segmentation of optic disc and cup from SD-OCT and color fundus photographs using a machine-learning graph-based approach, IEEE Trans Med Imaging, 34, 1854, 10.1109/TMI.2015.2412881 Dua, 2012, Wavelet-based energy features for glaucomatous image classification, IEEE Trans Inf Technol Biomed, 16, 80, 10.1109/TITB.2011.2176540 Youssif, 2008, Optic disc detection from normalized digital fundus images by means of a vessels direction matched filter, IEEE Trans Med Imaging, 27, 11, 10.1109/TMI.2007.900326 Mary, 2015, An empirical study on optic disc segmentation using an active contour model, Biomed Signal Process Control, 18, 19, 10.1016/j.bspc.2014.11.003 Haleem, 2017, A novel adaptive deformable model for automated optic disc and cup segmentation to aid Glaucoma diagnosis, J Med Syst, 42 Mohamed, 2015, On analyzing various density functions of local binary patterns for optic disc segmentation, 2015 IEEE Symposium on Computer Applications & Industrial Electronics (ISCAIE), 10.1109/ISCAIE.2015.7298324 Sarkar, 2017, Automated glaucoma detection of medical image using biogeography based optimization, Springer Proceedings in Physics Advances in Optical Science and Engineering, 381, 10.1007/978-981-10-3908-9_46 Kothari, 2009 Thakur, 2018, Survey on segmentation and classification approaches of optic cup and optic disc for diagnosis of glaucoma, Biomed Signal Process Control, 42, 162, 10.1016/j.bspc.2018.01.014 Nergiz, 2018, Automated fuzzy optic disc detection algorithm using branching of vessels and color properties in fundus images, Biocybern Biomed Eng, 38, 850, 10.1016/j.bbe.2018.08.003 Jiang, 2017, Fast, accurate and robust retinal vessel segmentation system, Biocybern Biomed Eng, 37, 412, 10.1016/j.bbe.2017.04.001 Kausu, 2018, Combination of clinical and multiresolution features for glaucoma detection and its classification using fundus images, Biocybern Biomed Eng, 38, 329, 10.1016/j.bbe.2018.02.003 Soorya, 2018, An automated and robust image processing algorithm for glaucoma diagnosis from fundus images using novel blood vessel tracking and bend point detection, Int J Med Inform, 110, 52, 10.1016/j.ijmedinf.2017.11.015 Jiang, 2017, Fast, accurate and robust retinal vessel segmentation system, Biocybern Biomed Eng, 37, 412, 10.1016/j.bbe.2017.04.001 Schoonjans, 1995, MedCalc: a new computer program formedical statistics, Comp Method Prog Biomed, 48, 257, 10.1016/0169-2607(95)01703-8 Fumero, 2015, Interactive tool and database for optic disc and cup segmentation of stereo and monocular retinal fundus images, Short Papers Proceedings–WSCG, 91 Uribe Valencia, 2019, Automated Optic Disc region location from fundus images: using local multi-level thresholding, best channel selection, and an Intensity Profile Model, Biomed Signal Process Control, 51, 148, 10.1016/j.bspc.2019.02.006 Jaikla, 2018, Segmentation of optic disc and cup in fundus images using maximally stable extremal regions, 2018 International Workshop on Advanced Image Technology (IWAIT) IEEE, 1 Zhou, 2018, Automatic optic disc detection in color retinal images by local feature spectrum analysis, Comput Math Methods Med, 2018, 1 Nergiz, 2018, Automated fuzzy optic disc detection algorithm using branching of vessels and color properties in fundus images, Biocybern Biomed Eng, 38, 850, 10.1016/j.bbe.2018.08.003 Elbalaoui, 2018, Segmentation of optic disc in fundus images using an active contour, J Electr Commerce Organ (JECO), 16, 97, 10.4018/JECO.2018010108 Xue, 2018, Optic disk detection and segmentation for retinal images using saliency model based on clustering, J Comput (Taipei), 29, 66 Rust, 2017, A robust algorithm for optic disc segmentation and fovea detection in retinal fundus images, Curr Dir Biomed Eng, 3, 533, 10.1515/cdbme-2017-0113 Chakravarty, 2017, Joint optic disc and cup boundary extraction from monocular fundus images, Comput Methods Programs Biomed, 147, 51, 10.1016/j.cmpb.2017.06.004 Panda, 2017, Robust and accurate optic disk localization using vessel symmetry line measure in fundus images, Biocybern Biomed Eng, 37, 466, 10.1016/j.bbe.2017.05.008 Akyol, 2016, Automatic detection of optic disc in retinal image by using keypoint detection, texture analysis, and visual dictionary techniques, Comput Math Methods Med, 10.1155/2016/6814791 Giachetti, 2014, Accurate and reliable segmentation of the optic disc in digital fundus images, J Med Imaging, 1, 10.1117/1.JMI.1.2.024001 Salazar Gonzalez, 2017, Segmentation of the blood vessels and optic disk in retinal images, IEEE J Biomed Health Inform, 18, 1874, 10.1109/JBHI.2014.2302749 Sevastopolsky, 2017, Optic disc and cup segmentation methods for glaucoma detection with modification of U-Net convolutional neural network, Pattern Recognit Image Anal, 27, 618, 10.1134/S1054661817030269 Quigley, 1990, The size and shape of the optic disc in normal human eyes, Arch Ophthalmol, 108, 51, 10.1001/archopht.1990.01070030057028 Li, 2018, Learning supervised descent directions for optic disc segmentation, Neurocomputing, 275, 350, 10.1016/j.neucom.2017.08.033 Thakur, 2019, Optic disc and optic cup segmentation from retinal images using hybrid approach, Expert Syst Appl, 127, 308, 10.1016/j.eswa.2019.03.009 Al-Bander, 2018, Dense fully convolutional segmentation of the optic disc and cup in colour fundus for glaucoma diagnosis, Symmetry, 10, 87, 10.3390/sym10040087 Esedoglu, 2002, Digital inpainting based on the Mumford–Shah–Euler image model, Eur J Appl Math, 13, 10.1017/S0956792502004904 Mienye, 2019, Prediction performance of improved decision tree-based algorithms: a review, Procedia Manuf, 35, 698, 10.1016/j.promfg.2019.06.011