Varnousfaderani Ehsan S, Yousefi Siamak, Bowd Christopher, Belghith Akram, Goldbaum Michael H
Hamilton Glaucoma Center and the Department of Ophthalmology, University of California San Diego, La Jolla, CA, USA.
AMIA Annu Symp Proc. 2015 Nov 5;2015:1140-7. eCollection 2015.
Blood vessel segmentation is important for the analysis of ocular fundus images for diseases affecting vessel caliber, occlusion, leakage, inflammation, and proliferation. We introduce a novel supervised method to evaluate performance of Leung-Malik filters in delineating vessels. First, feature vectors are extracted for every pixel with respect to the response of Leung-Malik filters on green channel retinal images in different orientations and scales. A two level hierarchical learning framework is proposed to segment vessels in retinal images with confounding disease abnormalities. In the first level, three expert classifiers are trained to delineate 1) vessels, 2) background, and 3) retinal pathologies including abnormal pathologies such as lesions and anatomical structures such as optic disc. In the second level, a new classifier is trained to detect vessels and non-vessel pixels based on results of the expert classifiers. Qualitative evaluation shows the effectiveness of the proposed expert classifiers in modeling retinal pathologies. Quantitative results on two standard datasets STARE (AUC = 0.971, Acc=0.927) and DRIVE (AUC = 0.955, Acc =0.903) are comparable with other state-of-the-art vessel segmentation methods.
血管分割对于分析眼底图像中影响血管管径、阻塞、渗漏、炎症和增殖的疾病非常重要。我们引入了一种新颖的监督方法来评估梁 - 马利克滤波器在描绘血管方面的性能。首先，针对绿色通道视网膜图像在不同方向和尺度上的梁 - 马利克滤波器响应，为每个像素提取特征向量。提出了一种两级分层学习框架，用于分割具有混杂疾病异常的视网膜图像中的血管。在第一级，训练三个专家分类器来描绘：1）血管，2）背景，3）视网膜病变，包括病变等异常病变和视盘等解剖结构。在第二级，基于专家分类器的结果训练一个新的分类器来检测血管和非血管像素。定性评估表明所提出的专家分类器在对视网膜病变建模方面的有效性。在两个标准数据集STARE（AUC = 0.971，Acc = 0.927）和DRIVE（AUC = 0.955，Acc = 0.903）上的定量结果与其他现有最先进的血管分割方法相当。
