Aiming at the problem of large workload and strong subjectivity for manual retinal vessels extraction, this paper proposes a retinal vessel segmentation method that combines regional growing strategy, pulse coupled neural network (PCNN), a Gaussian filter bank and a Gabor filter. First, 2D Gaussian filter bank and 2D Gabor filter are combined to enhance the shape retinal blood vessel region and strengthen the contrast between the blood vessel and the background. Then, PCNN with fast linking mechanism and region growing idea is implemented to achieve automatic retinal vessel segmentation in which the unprocessed pixel with highest intensity is set as the seed, and the adaptive linking weight and stop conditions are adopted. The experimental results on the DRIVE fundus database show that the average accuracy, sensitivity and specificity are 93.96%, 78.64%, 95.64%, respectively. The segmentation results have less vascular breakpoints and clear micro-vessels. This work has promising application value.
Retinal vascular segmentation combined with PCNN and morphological matching enhancement
First published at:Apr 01, 2019
1 Marin D, Aquino A, Gegunde-Zarias M E, et al. A new supervised 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. DOI:10.1109/TMI.2010.2064333
2 Zhao Y Q, Wang X H, Wang X F, et al. Retinal vessels segmentation based on level set and region growing[J]. Pattern Recognition, 2014, 47(7):2437-2446. DOI:10.1016/j.patcog.2014.01.006
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.
4 Chaudhuri S, Chatterjee S, Katz N, et al. Detection of blood vessels in retinal images using two-dimensional matched filters[J]. IEEE Transactions on Medical Imaging, 1989, 8(3):263-269. DOI:10.1109/42.34715
5 Jiang X Y, Mojon D. Adaptive local thresholding by verification-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. DOI:10.1109/TPAMI.2003.1159954
6 Gang L, Chutatape O, Krishnan S M. Detection and measurement of retinal vessels in fundus images using amplitude modified second-order Gaussian filter[J]. IEEE Transactions on Biomedical Engineering, 2002, 49(2):168-172. DOI:10.1109/10.979356
7 Zhang B, Zhang L, Zhang L, et al. Retinal vessel extraction by matched filter with first-order derivative of Gaussian[J]. Computers in Biology and Medicine, 2010, 40(4):438-445. DOI:10.1016/j.compbiomed.2010.02.008
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. DOI:10.1109/TMI.2006.879967
9 Gwetu M V, Tapamo J R, Viriri S. Segmentation of retinal blood vessels using normalized Gabor filters and automatic thresholding[J]. South African Computer Journal, 2014, 55(1):12-24. DOI:10.18489/sacj.v55i0.228
10 Zhang L, Fisher M, Wang W J. Retinal vessel segmentation using multi-scale textons derived from keypoints[J]. Computerized Medical Imaging and Graphics, 2015, 45:47-56. DOI:10.1016/j.compmedimag.2015.07.006
11 Zou P, Chan P, Rockett P. A model-based consecutive scanline tracking method for extracting vascular networks from 2-D digital subtraction angiograms[J]. IEEE Transactions on Medical Imaging, 2009, 28(2):241-249. DOI:10.1109/TMI.2008.929100
12 Vlachos M, Dermatas E. Multi-scale retinal vessel segmentation using line tracking[J]. Computerized Medical Imaging and Graphics, 2010, 34(3):213-227. DOI:10.1016/j.compmedimag.2009.09.006
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. DOI:10.1109/83.931095
14 Espona L, Carreira M J, Penedo M G, et al. Retinal vessel tree segmentation using a deformable contour model[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 application to medical image processing[J]. Journal of Electronics & Information Technology, 2007, 29(10):2316-2320.
16 Zhu S W, Hao C Y. An approach for fabric defect image segmentation based on the improved conventional PCNN model[J]. Acta Electronica Sinica, 2012, 40(3):611-616. DOI:10.3969/j.issn.0372-2112.2012.03.034
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. DOI:10.3969/j.issn.0372-2112.2012.03.034
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 Optoelectronics·Laser, 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]. Computers & Electrical Engineering, 2015, 46:528-538. DOI:10.1016/j.compeleceng.2015.03.028
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. DOI:10.1016/j.amc.2008.05.029
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.
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 histogram equalization (CLAHE) for real-time image enhancement[J]. Journal of VLSI Signal Processing Systems for Signal, Image and Video Technology, 2004, 38(1):35-44. DOI:10.1023/B:VLSI.0000028532.53893.82
23 Gwetu M V, Tapamo J R, Viriri S. Segmentation of retinal blood vessels using normalized Gabor filters and automatic thresholding[J]. South African Computer Journal, 2014, 53(55):12-24. DOI:10.18489/sacj.v55i0.228
24 Yao C, Chen H J. Automated blood vessel network segmentation in pathological retinal images[J]. Acta Electronica Sinica, 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.
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. DOI:10.3321/j.issn:0372-2112.2005.04.014
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. DOI:10.3321/j.issn:0372-2112.2005.04.014
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. DOI:10.3788/OPE.20172503.0756
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. DOI:10.3788/OPE.20172503.0756
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. DOI:10.1109/TNN.2002.804229
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. DOI:10.3321/j.issn:1000-436X.2002.01.007
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. DOI:10.3321/j.issn:1000-436X.2002.01.007
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. DOI:10.3321/j.issn:0372-2112.2008.05.025
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. DOI:10.3321/j.issn:0372-2112.2008.05.025
National Natural Science Foundation of China (61402259, 61272236, U1401252) and Yichang Applied Basic Research Project (A19-302-13)
Get Citation: Xu Guangzhu, Wang Yawen, Hu Song, et al. Retinal vascular segmentation combined with PCNN and morphological matching enhancement[J]. Opto-Electronic Engineering, 2019, 46(4): 180466.
Previous: Research on a 30 times ratio continuous zoom television optical system adjustment technology
Next: A feasibility study of using fiber-optic Raman spectrum system for fast diagnosis of gastric cancer