论文标题
使用ANN的视网膜血管结构进行监督分割
Supervised Segmentation of Retinal Vessel Structures Using ANN
论文作者
论文摘要
在这项研究中，使用人工神经网络（ANN）在RGB图像的绿色通道上进行了监督的视网膜血管分割过程。首选绿色通道，因为视网膜血管结构可以最清楚地与RGB图像的绿色通道区分开。该研究是使用驱动器数据集中的20张图像进行的，这是已知的最常见的视网膜数据集之一。这些图像经历了一些预处理阶段，例如对比度有限的自适应直方图均衡（CLAHE），颜色强度调整，形态学作业以及中位数和高斯滤波，以获得良好的分割。视网膜血管结构用顶帽和bot帽形态操作突出显示，并使用全局阈值转换为二进制图像。然后，通过指定为训练图像的图像的二进制版本对网络进行培训，目标是专家手动分割的图像。发现20张图像的平均分割精度为0.9492。
In this study, a supervised retina blood vessel segmentation process was performed on the green channel of the RGB image using artificial neural network (ANN). The green channel is preferred because the retinal vessel structures can be distinguished most clearly from the green channel of the RGB image. The study was performed using 20 images in the DRIVE data set which is one of the most common retina data sets known. The images went through some preprocessing stages like contrastlimited adaptive histogram equalization (CLAHE), color intensity adjustment, morphological operations and median and Gaussian filtering to obtain a good segmentation. Retinal vessel structures were highlighted with top-hat and bot-hat morphological operations and converted to binary image by using global thresholding. Then, the network was trained by the binary version of the images specified as training images in the dataset and the targets are the images segmented manually by a specialist. The average segmentation accuracy for 20 images was found as 0.9492.
