基于Hessian矩阵和熵的眼底图像血管分割

为了实现眼底图像血管自动准确分割,研究了一种基于Hessian矩阵线状滤波和熵阈值的分割方法.采用基于Hessian矩阵的多尺度线状滤波增强血管区域,结合滤波后灰度和具有方向性的线状邻域内灰度均值建立二维直方图,再根据直方图的最大类熵确定阈值,得到血管的二值化分割结果.实验表明,相比其它两种已有方法,提出的方法能够自动地得到更完整、更准确的眼底图像血管分割结果.

Vessel Segmentation in Fundus Images Based on Hessian Matrix and Entropy

Vessel segmentation is critical to image processing of fundus images,which is precursor and essential first step to further analysis and diagnosis of diseases.However,this remains a challenge due to the noise and intensity variation in the background of fundus images.In this study,we propose a novel vessel segmentation approach using Hessian-based linear filter and entropic thresholding method to automatically segment vessels on fundus images.Firstly we adapt multi-scale linear filtering based on Hessian matrix to enhance vessels.Further,we generate a novel two-dimensional histogram to capture gray level and directional average gray level of linear neighborhood in results from filtering,and then the threshold values are determined by the maximum class entropies according to this histogram.Finally,the binary segmentation results for the vessels are achieved.The experiments demonstrate that,compared with two other methods,the proposed method can automatically yield more complete and accurate results.