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Automated retinal blood vessels segmentation based on simplified PCNN and fast 2D-Otsu algorithm

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Abstract

According to the characteristics of dynamic firing in pulse coupled neural network (PCNN) and regional configuration in retinal blood vessel network, a new method combined with simplified PCNN and fast 2D-Otsu algorithm was proposed for automated retinal blood vessels segmentation. Firstly, 2D Gaussian matched filter was used to enhance the retinal images and simplified PCNN was employed to segment the blood vessels by firing neighborhood neurons. Then, fast 2D-Otsu algorithm was introduced to search the best segmentation results and iteration times with less computation time. Finally, the whole vessel network was obtained via analyzing the regional connectivity. Experiments implemented on the public Hoover database indicate that this new method gets a 0.803 5 true positive rate and a 0.028 0 false positive rate on an average. According to the test results, compared with Hoover algorithm and method of PCNN and 1D-Otsu, the proposed method shows much better performance.

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References

  1. YAO Chang, CHEN Hou-jin, LI Ju-peng. Segmentation of retinal blood vessels based on transition region extraction [J]. Acta Electronica Sinica, 2008, 36(5): 974–978. (in Chinese)

    Google Scholar 

  2. CHAUDHURI S, CHATTERJEE S, KATZ N, NELSON M, GOLDBAUM M. Detection of blood vessels in retinal images using two-dimensional matched filters [J]. IEEE Transactions on Medical Imaging, 1989, 8(3): 263–269.

    Article  Google Scholar 

  3. 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.

    Article  Google Scholar 

  4. TOLIAS Y A, PANAS S M. A fuzzy vessel tracking algorithm for retinal images based oil fuzzy clustering [J]. IEEE Transactions on Medical Imaging, 1998, 17(2): 263–273.

    Article  Google Scholar 

  5. TANG Min, WANG Hui-nan. Automatic segmentation algorithm of color retinal vascular images [J]. Chinese Journal of Scientific Instrument, 2007, 28(7): 1281–1285. (in Chinese)

    MathSciNet  Google Scholar 

  6. HOOVER A, KOUZNETSOVA V, GOLDBAUM M. Locating blood vessels in retinal images by piecewise threshold probing of a matched filter response [J]. IEEE Transactions on Medical Imaging, 2000, 19(3): 203–210.

    Article  Google Scholar 

  7. ECKHORN R, REITBOECK H J, ARNDT M, DICKE P. Feature linking via synchronization among distributed assemblies: Simulations of results from cat visual cortex [J]. Neural Computation, 1990, 2(3): 293–307.

    Article  Google Scholar 

  8. ZHANG Hong-liang, ZOU Zhong, LI Jie, CHEN Xiang-tao. Flame image recognition of alumina rotary kiln by artificial neural network and support vector machine methods [J]. Journal of Central South University of Technology, 2008, 15(1): 39–43.

    Article  Google Scholar 

  9. JOHNSON J L, PADGETT M L. PCNN models and applications [J]. IEEE Transactions on Neural Networks, 1999, 10(3): 480–498.

    Article  Google Scholar 

  10. MA Yi-de, DAI Ruo-lan, LI Lian. Automated image segmentation using pulse coupled neural networks and image’s entropy [J]. Journal on Communication, 2002, 23(1): 46–51. (in Chinese)

    Google Scholar 

  11. LI Guo-you, LI Hui-guang, WU Ti-hua. Enhancement of image based on Otsu and modified PCNN [J]. Journal of System Simulation, 2005, 17(6): 1370–1372. (in Chinese)

    Google Scholar 

  12. YAO Chang, CHEN Hou-jin, YU Jiang-bo, LI Ju-peng. Application of distributed genetic algorithm based on migration strategy in image segmentation [C]// The 3rd International Conference on Natural Computation. Haikou, 2007: 218–222.

  13. HOOVER A. Structured analysis of the retina [EB/OL]. [2000-11]. http://www.ces.clemson.edu/~ahoover/stare/.

  14. OTSU N. A threshold selection method from gray level histograms [J]. IEEE Transactions on Systems, Man and Cybernetics, 1979, 9(1): 62–66.

    Article  MathSciNet  Google Scholar 

  15. HUANG Shu-ying, ZHANG Er-hu. A method for segmentation of retinal image vessels [C]// Proceedings of the 6th World Congress on Intelligent Control and Automation. Dalian, 2006: 9673–9676.

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Author notes

  1. Chang Yao  (姚 畅)

    Present address: School of Electronics and Information Engineering, Beijing Jiaotong University, Beijing, 100044, China

Authors and Affiliations

  1. School of Electronics and Information Engineering, Beijing Jiaotong University, Beijing, 100044, China

    Hou-jin Chen  (陈后金)

Authors
  1. Chang Yao  (姚 畅)
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  2. Hou-jin Chen  (陈后金)
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Additional information

Foundation item: Project (60872081) supported by the National Natural Science Foundation of China; Project (50051) supported by the Program for New Century Excellent Talents in University; Project (4092034) supported by the Natural Science Foundation of Beijing

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Yao, C., Chen, Hj. Automated retinal blood vessels segmentation based on simplified PCNN and fast 2D-Otsu algorithm. J. Cent. South Univ. Technol. 16, 640–646 (2009). https://doi.org/10.1007/s11771-009-0106-3

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  • DOI: https://doi.org/10.1007/s11771-009-0106-3

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