改进型PCNN算法在眼底黄斑水肿区分割中的应用

Application of Improved PCNN Algorithm in Retinal Macular Edema Segmentation

为了从黄斑的OCT图像提取黄斑水肿的轮廓,估算水肿区体积,需精确地分割该区域。利用了改进的脉冲耦合神经网络(PCNN)算法,通过自适应选择基准阈值以及简化神经网络参数的方法,分割出OCT图像中的眼底黄斑水肿二值图。根据其图像信息熵值最大原则,确定最佳迭代次数为8次。采用误分率进行性能评价。图像仿真实验结果表明,算法能够快速准确提取黄斑水肿区,为OCT图像后续的分析提供依据。

In order to extract the outlines of macular edema from OCT images of maculars, and estimate the volume of edema, we have to accurately segment the macular edema region. In this paper, an improved PCNN algorithm was proposed to conduct the above process. Combined with the adaptive base threshold, and the simplified neural network parameters, a binary image of macular edema was produced. According to the principle of maximum image entropy, the optimal number of iterations was determined as 8, which was evaluated by its misclassification rate. Simulation showed that the proposed algorithm could extract the macular edema region rapidly and accurately, providing the basis for furhter OCT image analysis.