Holoentropy enabled-decision tree for automatic classification of diabetic retinopathy using retinal fundus images

References

[1] Acharya UR, Chua KC, Ng EYK, Yu W, Chee C. Application of higher order spectra for the identification of diabetes retinopathy stages. J Med Syst 2008; 32: 481–488.10.1007/s10916-008-9154-8Suche in Google Scholar

[2] Antal B, Hajdu A. An ensemble-based system for automatic screening of diabetic retinopathy. Knowl-Based Syst 2014; 60: 20–27.10.1016/j.knosys.2013.12.023Suche in Google Scholar

[3] Bresnick GH, Mukamel DB, Dickinson JC, Cole DR. A screening approach to the surveillance of patients with diabetes for the presence of vision-threatening retinopathy. Opthalmology 2000; 107: 19–24.10.1016/S0161-6420(99)00010-XSuche in Google Scholar

[4] Chiulla TA, Amador AG, Zinman B. Diabetic retinupathy and diabetic mucular edema: purhophysiology, screening, and novel therapies-review article. Diabetes Care 2003; 26: 2653–2664.10.2337/diacare.26.9.2653Suche in Google Scholar PubMed

[5] DIARETDB0 database from http://www.it.lut.fi/project/imageret/diaretdb0/.Suche in Google Scholar

[6] DIARETDB1 database from http://www.it.lut.fi/project/imageret/diaretdb1/index.html.Suche in Google Scholar

[7] Dupas B, Walter T, Erginay A, et al. Evaluation of automated fundus photograph analysis algorithms for detecting microaneurysms haemorrhages, and exudates of a computer-assisted diagnostic system for grading diabetic retinopathy. Diabetes Metab 2010; 36: 213–220.10.1016/j.diabet.2010.01.002Suche in Google Scholar PubMed

[8] Frank RN. Diabetic retinopathy. Prog Retin Eye Res 1995; 14: 361–392.10.1007/978-1-4020-6780-8_21Suche in Google Scholar

[9] Fuente-Arriagaa JA, Felipe-Riverónb EM, Garduño-Calderónc E. Application of vascular bundle displacement in the optic disc for glaucoma detection using fundus images. Comput Biol Med 2014; 47: 27–35.10.1016/j.compbiomed.2014.01.005Suche in Google Scholar PubMed

[10] Fumero F, Alayon S, Sanchez JL, Sigut J. RIM-ONE: an open retinal image database for optic nerve evaluation. International Symposium on Computer-Based Medical Systems (CBMS) 2011.10.1109/CBMS.2011.5999143Suche in Google Scholar

[11] Gardner GG, Keating D, Williamson TH, Elliott AT. Automatic detection of diabetic retinopathy using an artificial neural network: a screening tool. Br J Ophthalmol 1996; 80: 940–944.10.1136/bjo.80.11.940Suche in Google Scholar PubMed PubMed Central

[12] Hu Q, Che X, Zhang L, Zhang D, Guo M, Yu D. Rank entropy-based decision trees for monotonic classification. IEEE Trans Knowl Data Eng 2012; 24: 2052–2064.10.1109/TKDE.2011.149Suche in Google Scholar

[13] Hu HW, Chen YL, Tang K. A novel decision-tree method for structured continuous-label classification. IEEE Trans Cybern 2013; 43: 1734–1746.10.1109/TSMCB.2012.2229269Suche in Google Scholar PubMed

[14] Kavitha M, Palani S. Hierarchical classifier for soft and hard exudates detection of retinal fundus images. J Intell Fuzzy Syst 2014; 27: 2511–2528.10.3233/IFS-141224Suche in Google Scholar

[15] Kavitha M, Palani S. Blood vessel, optical disk and damage area-based features for diabetic detection from retinal images. Arab J Sci Eng 2014; 39: 7059–7071.10.1007/s13369-014-1255-8Suche in Google Scholar

[16] Lahmiri S, Boukadoum M. Hybrid discrete wavelet transform and Gabor filter banks processing for features extraction from biomedical images. J Med Eng 2013; 2013: 1–13.10.1155/2013/104684Suche in Google Scholar PubMed PubMed Central

[17] Lahmiri S, Boukadoum M. Automated detection of circinate exudates in retina digital images using empirical mode decomposition and the entropy and uniformity of intrinsic mode functions. Biomed Eng-Biomed Tech 2014; 59: 357–366.10.1515/bmt-2013-0082Suche in Google Scholar PubMed

[18] Lahmiri S, Gargour C, Gabrea M. Automated pathologies detection in retina digital images based on the complex continuous wavelet transform phase angles. Healthc Technol Lett 2014; 1: 104–108.10.1049/htl.2014.0068Suche in Google Scholar PubMed PubMed Central

[19] Larsen N, Godt J, Grunkin M, Larsen M. Automated detection of diabetic retinopathy in a fundus photographic screening population. Invest Ophthalmol Visual Sci 2003; 44: 767–771.10.1167/iovs.02-0417Suche in Google Scholar PubMed

[20] Li H, Chutatape O. Automated feature extraction in color retinal images by a model based approach. IEEE Trans Biomed Eng 2004; 51: 246–254.10.1109/TBME.2003.820400Suche in Google Scholar PubMed

[21] Mane VM, Jadhav DV. Progress towards automated early stage detection of diabetic retinopathy: image analysis systems and potential. J Med Biol Eng 2014; 34: 520–527.Suche in Google Scholar

[22] Miranda AA, Le Borgne YA, Bontempi G. New routes from minimal approximation error to principal components. Neural Process Lett 2008; 27: 197–207.10.1007/s11063-007-9069-2Suche in Google Scholar

[23] Mookiah MRK, Acharya UR, Martis RJ, et al. Evolutionary algorithm based classifier parameter tuning for automatic diabetic retinopathy grading: a hybrid feature extraction approach. Knowl Based Syst 2013; 39: 9–22.10.1016/j.knosys.2012.09.008Suche in Google Scholar

[24] Muthu Rama Krishnan M, Rajendra Acharya U, Lim CM, Petznick A, Suri JS. Data mining technique for automated diagnosis of glaucoma using higher order spectra and wavelet energy features. Knowl-Based Syst 2012; 33: 73–82.10.1016/j.knosys.2012.02.010Suche in Google Scholar

[25] Rajendra Acharya U, Ng EYK, Eugene LWJ, et al. Decision support system for the glaucoma using Gabor transformation. Biomed Signal Process Control 2015; 15: 18–26.10.1016/j.bspc.2014.09.004Suche in Google Scholar

[26] Ravishankar S, Jain A, Mittal A. Automated feature extraction for early detection of diabetic retinopathy in fundus images. IEEE conference on Computer Vision and Pattern Recognition 2009; 210–217: 10.1109/CVPR.2009.5206763.Suche in Google Scholar

[27] Sinthanayothin C, Kongbunkiat V, Phoojaruenchanachai S, Singalavanija A. Automated screening system for diabetic retinopathy, in: Proceedings of the 3rd International Symposium on Image and Signal Processing and Analysis, ISPA2003, 2003; 2: 915–920.10.1109/ISPA.2003.1296409Suche in Google Scholar

[28] Susman EJ, Tsiaras WJ, Soper KA. Diagnosis of diabetic eye disease. J Am Med Assoc 1982; 247: 3231–3234.10.1001/jama.1982.03320480047025Suche in Google Scholar

[29] Usman Akram M, Shehzad K, Tariq A, Khan SA, Azam F. Detection and classification of retinal lesions for grading of diabetic retinopathy. Comput Biol Med 2014; 45: 161–171.10.1016/j.compbiomed.2013.11.014Suche in Google Scholar PubMed

[30] Vega R, Sanchez-Ante G, Falcon-Morales LE, Sossa H, Guevara E. Retinal vessel extraction using Lattice Neural Networks with dendritic processing. Comput Biol Med 2015; 58: 20–30.10.1016/j.compbiomed.2014.12.016Suche in Google Scholar PubMed

[31] Walter T, Klein JC, Massin P, Erginay A. A contribution of image processing to the diagnosis of diabetic retinopathy detection of exudates in color fundus images of the human retina. IEEE Trans Med Imaging 2002; 21: 1236–1243.10.1109/TMI.2002.806290Suche in Google Scholar PubMed

[32] Welikala RA, Dehmeshki J, Hoppea A, et al. Automated detection of proliferative diabetic retinopathy using a modified line operator and dual classification. Comput Methods Programs Biomed 2014; 114: 247–261.10.1016/j.cmpb.2014.02.010Suche in Google Scholar PubMed

[33] Wilfred Franklin S, Edward Rajan S. Computerized screening of diabetic retinopathy employing blood vessel segmentation in retinal images. Biocybern Biomed Eng 2014; 34: 117–124.10.1016/j.bbe.2014.01.004Suche in Google Scholar

[34] Wu S, Wang S. Information-theoretic outlier detection for large-scale categorical data. IEEE Trans Knowl Data Eng 2013; 25: 589–602.10.1109/TKDE.2011.261Suche in Google Scholar