Retinal vessel in fundus image is segmented with a comprehensive method for classification. The method is processed in four phases namely, preprocessing, segmentation, features extraction and classification, this method can be used on different images sets. Retinal vessels are enhanced by brightness preserving dynamic fuzzy histogram equalization (BPDFHE), separating enhanced image is used to detect the retinal diseases. Then these enhanced images are segmented by using quincunx wavelet decomposition for extracting features like first order statistics and gray level co-occurrence matrix (GLCM). The feature vector encodes information to handle the normal and abnormal retinal image and those features are classified using different classifiers (Adaboost, DSVM, ELMASR, EPLS, KNN, NB, NBFFS, OCPLS, RBFN, RF, SOWA, SVM and SVNN) and the performance is evaluated in detail. Blood vessel segmentation with this method is effective for retinal image computational analyses such as early retinal disease detection. Experimental results on three public retinal data sets like DRIVE, STARE and MESSIDOR and real time images are taken from Agarwal’s Eye Hospital, Tirunelveli, demonstrating an excellent performance in comparison with retinal vessel segmentation methods reported recently.

用全面的分类方法对眼底图像中的视网膜血管进行分割。该方法分为预处理，分割，特征提取和分类四个阶段，该方法可用于不同的图像集。通过保持亮度的动态模糊直方图均衡化（BPDFHE）增强视网膜血管，分离的增强图像用于检测视网膜疾病。然后，利用梅花形小波分解对这些增强图像进行分割，以提取诸如一阶统计量和灰度共生矩阵（GLCM）之类的特征。特征向量对信息进行编码以处理正常和异常的视网膜图像，并使用不同的分类器对这些特征进行分类（Adaboost，DSVM，ELMASR，EPLS，KNN，NB，NBFFS，OCPLS，RBFN，RF，SOWA，SVM和SVNN），并对性能进行了详细评估。用这种方法进行血管分割对视网膜图像计算分析（例如早期视网膜疾病检测）有效。在三个公共视网膜数据集（例如DRIVE，STARE和MESSIDOR）上的实验结果以及实时图像来自Tirunelveli的Agarwal眼科医院，与最近报道的视网膜血管分割方法相比，具有出色的性能。