[1] Marin D, Aquino A, Gegunde-Zarias M E, et al. A new super-vised method for blood vessel segmentation in retinal images by using gray-level and moment invariants-based features[J]. IEEE Transactions on Medical Imaging, 2011, 30(1): 146–158.

[2] Zhao Y Q, Wang X H, Wang X F, et al. Retinal vessels seg-mentation based on level set and region growing[J]. Pattern Recognition, 2014, 47(7): 2437–2446.

[3] Gu X D, Guo S D, Yu D H. New approach for noise reducing of image based on PCNN[J]. Journal of Electronics & Information Technology, 2002, 24(10): 1304–1309. 顾晓东, 郭仕德, 余道衡. 一种基于 PCNN的图像去噪新方法 [J]. 电子与信息报, 2002, 24(10): 1304–1309.

[4] Chaudhuri S, Chatterjee S, Katz N, et al. Detection of blood vessels in retinal images using two-dimensional matched fil-ters[J]. IEEE Transactions on Medical Imaging, 1989, 8(3): 263–269.

[5] Jiang X Y, Mojon D. Adaptive local thresholding by verifica-tion-based multithreshold probing with application to vessel detection in retinal images[J]. IEEE Transactions on Pattern Analysis and Machine Intelligence, 2003, 25(1): 131–137.

[6] Gang L, Chutatape O, Krishnan S M. Detection and measure-ment of retinal vessels in fundus images using amplitude mod-ified second-order Gaussian filter[J]. IEEE Transactions on Biomedical Engineering, 2002, 49(2): 168–172.

[7] Zhang B, Zhang L, Zhang L, et al. Retinal vessel extraction by matched filter with first-order derivative of Gaussian[J]. Com-puters in Biology and Medicine, 2010, 40(4): 438–445.

[8] Soares J V B, Leandro J J G, Cesar R M, et al. Retinal vessel segmentation using the 2-D Gabor wavelet and supervised classification[J]. IEEE Transactions on Medical Imaging, 2006, 25(9): 1214–1222.

[9] Gwetu M V, Tapamo J R, Viriri S. Segmentation of retinal blood vessels using normalized Gabor filters and automatic thre-sholding[J]. South African Computer Journal, 2014, 55(1): 12–24.

[10] Zhang L, Fisher M, Wang W J. Retinal vessel segmentation using multi-scale textons derived from keypoints[J]. Compute-rized Medical Imaging and Graphics, 2015, 45: 47–56.

[11] Zou P, Chan P, Rockett P. A model-based consecutive scanline tracking method for extracting vascular networks from 2-D dig-ital subtraction angiograms[J]. IEEE Transactions on Medical Imaging, 2009, 28(2): 241–249.

[12] Vlachos M, Dermatas E. Multi-scale retinal vessel segmenta-tion using line tracking[J]. Computerized Medical Imaging and Graphics, 2010, 34(3): 213–227.

[13] Zana F, Klein J C. Segmentation of vessel-like patterns using mathematical morphology and curvature evaluation[J]. IEEE Transactions on Image Process, 2001, 10(7): 1010–1019.

[14] Espona L, Carreira M J, Penedo M G, et al. Retinal vessel tree segmentation using a deformable contour mod-el[C]//Proceedings of the 19th International Conference on Pattern Recognition, 2008: 1–4.

[15] Yu J B, Chen H J. Improvement of PCNN model and its appli-cation to medical image processing[J]. Journal of Electronics & Information Technology, 2007, 29(10): 2316–2320.于江波, 陈后金. PCNN模型的改进及其在医学图像处理中的应用[J].电子与信息学报, 2007, 29(10): 2316–2320.

[16] Zhu S W, Hao C Y. An approach for fabric defect image seg-mentation based on the improved conventional PCNN model[J]. Acta Electronica Sinica, 2012, 40(3): 611–616.祝双武, 郝重阳. 一种基于改进型 PCNN的织物疵点图像自适应分割方法[J].电子学报, 2012, 40(3): 611–616.

[17] Yao C, Chen H J, Jing T, et al. Extraction of blood vessel tree in retinal image based on improved PCNN[J]. Journal of Optoe-lectronics·Laser, 2011, 22(11): 1745–1750. 姚畅, 陈后金, 荆涛, 等. 一种基于改进的 PCNN的视网膜血管树提取方法[J].光电子·激光, 2011, 22(11): 1745–1750.

[18] Jiang W, Zhou H Y, Shen Y, et al. Image segmentation with pulse-coupled neural network and Canny operators[J]. Com-puters & Electrical Engineering, 2015, 46: 528–538.

[19] Lu Y F, Miao J, Duan L J, et al. A new approach to image segmentation based on simplified region growing PCNN[J]. Applied Mathematics and Computation, 2008, 205(2): 807–814.

[20] Chen M M, Xiong X L, Zhang Y, et al. A new method for retinal fundus image enhancement[J]. Journal of Chongqing Medical University, 2014, 39(8): 1087–1090.陈萌梦, 熊兴良, 张琰, 等. 1种视网膜眼底图像增强的新方法[J]. 重庆医科大学学报, 2014, 39(8): 1087–1090.

[21] Oloumi F, Rangayyan R M, Oloumi F, et al. Digital image processing and pattern recognition techniques for the analysis of fundus images of the retina[R]. Alberta, Canada: Department of Electrical and Computer Engineering, University of Calgary, 2010: 8.

[22] Reza A M. Realization of the contrast limited adaptive histo-gram equalization (CLAHE) for real-time image enhance-ment[J]. Journal of VLSI Signal Processing Systems for Signal, Image and Video Technology, 2004, 38(1): 35–44.

[23] Gwetu M V, Tapamo J R, Viriri S. Segmentation of retinal blood vessels using normalized Gabor filters and automatic thre-sholding[J]. South African Computer Journal, 2014, 53(55): 12–24.

[24] Yao C, Chen H J. Automated blood vessel network segmenta-tion in pathological retinal images[J]. Acta Electronica Sinica, 2010, 38(5): 1226–1233. 姚畅, 陈后金. 病变视网膜图像血管网络的自动分割 [J].电子学报, 2010, 38(5): 1226–1233.

[25] Lindblad T, Kinser J M. Image Processing Using Pulse-Coupled Neural Networks: Applications in Python[M]. Xu G X, Ma Y D, Lei B J, trans. 3rd ed. Beijing: National Defense Industry Press, 2017: 1.托马斯·林德布拉德 , 詹森·金赛 . 图像处理与脉冲耦合神经网络: 基于 Python的实现[M].徐光柱, 马义德, 雷帮军, 译. 3版. 北京: 国防工业大学出版社, 2017: 1.

[26] Bi Y W, Qiu T S. An adaptive image segmentation method based on a simplified PCNN[J]. Acta Electronica Sinica, 2005, 33(4): 647–650.毕英伟, 邱天爽. 一种基于简化 PCNN的自适应图像分割方法[J].电子学报, 2005, 33(4): 647–650.

[27] Xu G Z, Zhang L, Zou Y B, et al. Retinal blood segmentation with adaptive PCNN and matched filter[J]. Optics and Precision Engineering, 2017, 25(3): 756–764. 徐光柱, 张柳, 邹耀斌, 等. 自适应脉冲耦合神经网络与匹配滤波器相结合的视网膜血管分割 [J].光学精密工程 , 2017, 25(3): 756–764.

[28] Stewart R D, Fermin I, Opper M. Region growing with pulse-coupled neural networks: an alternative to seeded region growing[J]. IEEE Transactions on Neural Networks, 2002, 13(6): 1557–1562.

[29] Ma Y D, Dai R L, Li L. Automated image segmentation using pulse coupled neural networks and image's entropy[J]. Journal of China Institute of Communications, 2002, 23(1): 46–51. 马义德 , 戴若兰, 李廉. 一种基于脉冲耦合神经网络和图像熵的自动图像分割方法[J].通信学报, 2002, 23(1): 46–51.

[30] Yao C, Chen H J, Li J P. Segmentation of retinal blood vessels based on transition region extraction[J]. Acta Electronica Sinica, 2008, 36(5): 974–978. 姚畅, 陈后金 , 李居朋 . 基于过渡区提取的视网膜血管分割方法 [J].电子学报, 2008, 36(5): 974–978.