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

作　　者：姚畅 陈后金 

机构地区：[1]School of Electronics and Information Engineering, Beijing Jiaotong University

出　　处：《Journal of Central South University》2009年第4期640-646,共7页中南大学学报（英文版）

基　　金：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

摘　　要：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.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.

关 键 词：blood vessel segmentation pulse coupled neural network （PCNN） OTSU NEURON 

分 类 号：R774[医药卫生—眼科]