基于匹配滤波和自动阈值的眼底血管分割方法

提出一种快速、简便、高效的眼底血管分割方法.分析眼底图像的灰度值分布和对比度变化,利用匹配滤波克服背景干扰,消除噪声影响,达到灰度均衡,实现眼底图像的亮度归一化.估计眼底图像中背景像素所占比例,利用直方图自动选择阈值,完成对眼底图像中血管的有效分割.在公开的眼底图像数据库上进行测试,该方法对眼底血管分割具有较好的性能指标.实验表明,提出的基于匹配滤波和阈值优化的眼底血管分割方法,准确率高、复杂度低,对眼科疾病的计算机辅助诊断有一定的实用价值.  

Retinal blood vessel segmentation for macula detachment surgery monitoring instruments

In recent years image processing has improved detection and diagnosis in medical application. Image processing applications are now embedded in medical instruments such as MRI and CT. In the case of retinopathy, fast extraction of blood vessels can allow the physician to view injury regions during surgery. Macula detachment surgeries, or computer‐aided intraocular surgeries, require precise and real‐time knowledge of the vasculature during the operation. Use of artificial neural network has produced good results in image processing applications, but its implementation may not be suitable for real‐time applications in small, embedded hardware. Because of error resiliency of the neural network, its structure can be pruned and simplified. In this paper an efficient hardware implementation of neural network for retinal vessel segmentation is proposed. We simplify the neural network structure in such a way that the accuracy of the results is not altered significantly. Simulation results and FPGA implementation show that our proposed network has low complexity and can be applied for segmentation of retinal vessels with acceptable accuracy. This makes the proposed method a good candidate to be implemented in any device such as a binocular indirect ophthalmoscope.