參考文獻 |
[1] World Health Organization, Top 10 cause of death,
http://www.who.int/en/news-room/fact-sheets/detail/the-top-10-causes-of-death
[2] International Diabetes Foundation. IDF Diabetes Atlas, 8th edn. Brussels, Belgium: International Diabetes Federation, 2017.
[3] Cho N.H., Shaw J.E. Karuranga S. et al. (2018) IDF Diabetes Atlas: Global estimates of diabetes prevalence for 2017 and projections for 2045
[4] American Diabetes Association. American Diabetes Association. (2013). Eye complications.
http://www.diabetes.org/living-withdiabetes/complications/eyecomplications/?referrer=https://www.google.com.tw/
[5] Wilkinson C P, Ferris F L, Klein R E, et al. (2003) Proposed international clinical diabetic retinopathy and diabetic macular edema disease severity scales. Ophthalmology. 110: 1677-1682
[6] Tufail A, Rudisill C, Egan C, et al. (2017) Automated diabetic retinopathy image assessment software: diagnostic accuracy and cost-effectiveness compared with human graders. Ophthalmology. 124: 343-351.
[7] Tufail A, Kapetanakis V V, Salas-Vega S, et al. (2016) An observational study to assess if automated diabetic retinopathy image assessment software can replace one or more steps of manual imaging grading and to determine their cost-effectiveness. Health Technol Assess. 20: xxviii-1.
[8] Liu I, Sun Y. (1993) Recursive tracking of vascular networks in angiograms based on the detection-deletion scheme. IEEE Trans. Med. Imag. 12: 334-341.
[9] Can A, Shen H, Turner J N, et al. (1999) Rapid automated tracing and feature extraction from retinal fundus images using direct exploratory algorithms. IEEE Trans. Inf. Technol. Biomed. 3: 125-138.
[10] Vlachos M, Dermatas E. (2010) Multi-scale retinal vessel segmentation using line tracking. Computerized Medical Imaging and Graphics. 34: 213-227.
[11] Yin Y, Adel M, Bourennane S. (2012) An automatic tracking method for retinal vascular tree extraction. Proceeding of IEEE International Conference on Acoustics, Speech, and Signal Processing, Kyoto Japan (2012). IEEE.
[12] Chaudhuri S, Chatterjee S, Katz N, et al. (1989) Detection of blood vessels in retinal images using two-dimensional matched filters. IEEE Trans. Med. Imag. 8: 263-269.
[13] Hoover A, Kouznetsova V, Goldbaum M. (2000) Locating blood vessels in retinal images by piecewise threshold probing of a matched filter response. IEEE Trans. Med. Imag. 19: 203-210
[14] Jiang X, Mojon D. (2003) Adaptive Local Thresholding by Verification-Based Multithreshold Probing with Application to Vessel Detection in Retinal Images. IEEE Trans. on Pattern Anal. and Mach. Intell. 25: 131-137.
[15] Zhang L, Li Q, You J, et al. (2009) A Modified Matched Filter With Double-Sided Thresholding for Screening Proliferative Diabetic Retinopathy. IEEE Trans. Inf. Technol. Biomed. 13: 528-534.
[16] Li Q, You J, Zhang D. (2012) Vessel segmentation and width estimation in retinal images using multiscale production of matched filter responses. Expert Systems with Applications. 39: 7600-7610.
[17] Zana F, Klein J C. (2001) Segmentation of vessel-like patterns using mathematical morphology and curvature evaluation. IEEE Trans. Image Process. 10: 1010-1019.
[18] Ayala G, Leon T, Zapater V. (2005) Different Averages of a Fuzzy Set With an Application to Vessel Segmentation. IEEE Trans. Fuzzy Syst. 13: 384-393.
[19] Miri M S, Mahloojifar A. (2011) Retinal image analysis using Curvelet transform and multistructure elements morphology by reconstruction. IEEE Trans. Biomed. Eng. 58: 1183-1192.
[20] Karthika D, Marimuthu A. (2014) Retinal image analysis using contourlet transform and multistructure elements morphology by reconstruction. Proceedings of World Congress on Computing and Communication Technologies, Tiruchirappalli, India (2014). IEEE.
[21] Kass M, Witkin A, Terzopoulos D. (1988) Snake: active contour models. International Journal of Computer Vision. 1: 321-331.
[22] Espona L, Carreira M J, Ortega M, et al. (2007) A Snake for Retinal Vessel Segmentation. Proceedings of the 3rd Iberian Conference on Pattern Recognition and Image Analysis, Girona Spain (2007). Springer-Verlag Berlin and Heidelberg GmbH & Co. KG, Berlin Germany.
[23] Al-Diri B, Hunter A. (2005) A ribbon of twins for extracting vessel boundaries. Proceedings of the 3rd European Medical and Biological Engineering Conference, Prague Czech Republic (2005).
[24] Zhang Y, Hsu W, Lee M L. (2009) Detection of retinal blood vessels based on nonlinear projections. Journal of Signal Processing Systems. 55: 103-112.
[25] Zhao Y Q, Wang X H, Wang X F, et al. (2014) Retinal vessels segmentation based on level set and region growing. Pattern Recognition. 47: 2437-2446.
[26] Roberto M. Cesar Jr, Jelinek Herbert F. (2003) Segmentation of retinal fundus vasculature in nonmydriatic camera images using wavelets. In: Angiography and Plaque Imaging. Laxminarayan S, Suri J S editors. CRC Press. 193-224.
[27] Leandro J J, Soares J V, Cesar R M, et al. (2003) Blood vessels segmentation in non-mydriatic images using wavelets and statistical classifiers. Proceedings of XVI Brazilian Symposium on Computer Graphics and Image Processing. Sao Carlos, Brazil (2003). IEEE.
[28] Ricci E, Perfetti R. (2007) Retinal Blood Vessel Segmentation Using Line Operators and Support Vector Classification. IEEE Trans. Med. Imag. 26: 1357-1365.
[29] Marin D, Aquino A, Gegundez-Arias M E, et al. (2011) A New Supervised Method for Blood Vessel Segmentation in Retinal Images by Using Gray-Level and Moment Invariants-Based Features. IEEE Trans. Med. Imag. 30: 146-158.
[30] Shanmugam V, Wahida Banu R S D. (2013) Retinal blood vessel segmentation using an Extreme Learning Machine approach. Proceedings of 2013 IEEE Point-of-Care Healthcare Technologies, Bangalore India (2013). IEEE.
[31] Wang S, Yin Y, Cao G, et al. (2015) Hierarchical retinal blood vessel segmentation based on feature and ensemble learning. Neurocomputing. 149: 708-717.
[32] Tolias Y A, Panas S M. (1998) A fuzzy vessel tracking algorithm for retinal images based on fuzzy clustering. IEEE Trans. Med. Imag. 17: 263-273.
[33] Xie S, Nie H. (2013) Retinal vascular image segmentation using genetic algorithm plus FCM clustering. Proceedings of 2013 Third International Conference on Intelligent System Design and Engineering Applications, Hong Kong, China (2013). IEEE.
[34] Salazar-Gonzalez A, Kaba D, Li Y, et al. (2014) Segmentation of the blood vessels and optic disk in retinal images. IEEE J. Biomed. Health Inform. 18: 1874-1886.
[35] Ege B M, Hejlesen O K, Larsen O V, et al. (2000) Screening for diabetic retinopathy using computer based image analysis and statistical classification. Computer Methods and Programs in Biomedicine. 62: 165-175.
[36] Silberman N, Ahrlich K, Fergus R, et al. (2010) Case for automated detection of diabetic retinopathy. Proceedings of AAAI Artificial Intelligence for Development, California USA (2010).
[37] Karegowda A G, Nasiha A, Jayaram M A, et al. (2011) Exudates detection in retinal images using back propagation neural network. International Journal of Computer Applications. 25: 25-31.
[38] Kavitha S, Duraiswamy K. (2011) Automatic detection of hard and soft exudates in fundus images using color histogram thresholding. European Journal of Science Research. 48: 493–504.
[39] Jorge de la Calleja, Tecuapetla L, Medina M A, et al. (2014) LBP and Machine Learning for Diabetic Retinopathy Detection. Proceedings of the 2014 Intelligent Data Engineering and Automated Learning, Salamanca Spain (2014). Springer.
[40] Fractional Max-Pooling. http://www2.warwick.ac.uk/fac/sci/statistics/staff/academic-research/graham/fmp.pdf
[41] LIBSVM: A Library for Support Vector Machines, C.-C. Chang and C.-J. Lin. http://www.csie.ntu.edu.tw/~cjlin/papers/libsvm.pdf
[42] Rao R V, Savsani V J, Vakharia D P. (2010) Teaching-learning-based optimization: A novel method for constrained mechanical design optimization problems. Computer-Aided Design. 43: 303-315.
[43] Kaggle contests: Identify signs of diabetic retinopathy in eye images.
https://www.kaggle.com/c/diabetic-retinopathy-detection
[44] Christian S, Vincent V, Sergey I, et al. (2015) Rethinking the Inception Architecture for Computer Vision. (2016) IEEE Conference on Computer Vision and Pattern Recognition.
[45] Kaiming H, Xiangyu, Shaoqing R, et al. (2015) Deep Residual Learning for Image Recognition. 2016 IEEE Conference on Computer Vision and Pattern Recognition.
[46] Francois C. (2016) Xception: Deep Learning with Depthwise Separable Convolutions. 2017 IEEE Conference on Computer Vision and Pattern Recognition. |