摘要
结合自适应脉冲耦合神经网络(PCNN)和最大类间方差准则,该文提出一种眼底图像血管自动检测方法。首先采用对比度受限的自适应直方图均衡化(CLAHE)和2维高斯匹配滤波对眼底图像进行预处理,以增强血管和背景的对比度;然后基于简化PCNN模型,结合最大类间方差准则对预处理后的眼底图像进行分割,针对PCNN神经元的链接强度通常为常数的不足,使用像素的拉普拉斯能量(EOL)作为对应神经元的链接强度值,使得PCNN能够根据像素特征自适应地调整神经元的链接强度;最后采用面积滤波、断点连接对分割结果进行后处理,得到最终的血管检测结果。对STARE眼底图像库的实验结果表明,该方法具有较高的鲁棒性、有效性和可靠性。
A new blood vessels automatic detection method in fundus image combing adaptive Pulse Coupled Neural Network （PCNN） and maximal categories variance criterion is proposed. In preprocessing, Contrast Limited Adaptive Histogram Equalization （CLAHE） and two-dimensional Gaussian matched filtering are adopted to improve the contrast between blood vessels and background. Then based on simplified PCNN model and maximal categories variance criterion, the preprocessed fundus image is segmented. In image processing, the linking strength of each PCNN neuron is usually a constant. In order to overcome the limitation, pixel’s Energy Of Laplace （EOL） is chosen as the linking strength of corresponding PCNN neuron, thus PCNN can adjust its linking strengths according to pixel features adaptively. Finally, the final blood vessels detection result is obtained via postprocessing including area filtering and breakpoint connection. The experiments implemented on the Hoover fundus image database show that the method has relatively higher robustness, effectiveness and reliability.
出处
《电子与信息学报》
EI
CSCD
北大核心
2013年第10期2411-2417,共7页
Journal of Electronics & Information Technology
基金
国家自然科学基金(61102150)
天津市科技支撑计划重点项目(12ZCZDGX02100)资助课题
