视网膜血管图像分割研究现状分析

Analysis of Current Research Status of Retinal Vessel Segmentation

视网膜血管图像分割是医疗图像研究中的一个重要课题,能够有效地辅助医生进行快速的心血管疾病、糖尿病等疾病的临床诊断和治疗。近年来,许多学者都在研究视网膜血管图像分割方面的研究内容并已经取得一定的成果。但是,由于视网膜图像的复杂性和图像采集过程中噪声和光照因素的影响,进行精确的视网膜血管图像分割仍然是一项具有挑战性的任务。介绍近几年若干种较为典型的视网膜血管图像分割技术,根据分割技术的特征学习方式不同,分为基于有监督和基于无监督的两类方法。对于每类分割方法,对其所涉及的关键技术进行阐释。基于两个公开数据集(DRIVE和STARE)的测试数据对若干典型视网膜血管图像分割方法的优缺点进行详细的分析。对将来工作进行简要的展望。

Vessel segmentation of retinal fundus images is an important topic in the medical image research.It is import to help doctors to diagnose and treat diseases such as diabetes and cardiovascular diseases.In recent years,many scholars have studied the retinal vessel segmentation algorithm and made some progress.However,due to the complexity of retinal images and the influence of noise and light factors in the im?age acquisition process,accurate retinal vessel segmentation is still a challenging task.Introduces a number of typical retinal vascular im?age segmentation techniques proposed in recent years.According to different segmentation methods,it can be divided into supervised meth?ods and unsupervised methods.For each kind of segmentation method,describes the key technologies.Moreover,based on the test results on two public datasets(DRIVE and STARE),analyses the advantages and disadvantages of these typical segmentation algorithms.