ELEMENT：基于耦合区域生长和机器学习方法的多模态视网膜血管分割
ELEMENT: Multi-Modal Retinal Vessel Segmentation Based on a Coupled Region Growing and Machine Learning Approach
AI总结：
本文提出了一种基于耦合区域生长和机器学习方法的多模态视网膜血管分割框架ELEMENT，通过区域生长和机器学习提取特征并进行像素分类，提高了分割的准确性和效率，实验表明其在多个数据集上均优于现有方法。
摘要：
视网膜血管结构包含重要的信息，用于检测和分析眼部疾病，包括年龄相关性黄斑变性、糖尿病视网膜病变和青光眼。常用的诊断模态包括视网膜摄影、扫描激光眼底镜（SLO）和荧光素血管造影（FA）。通常，视网膜血管分割是手动或交互式进行的，这使得过程耗时且容易出错。在本研究中，我们提出了一种新的多模态分割框架，称为ELEMENT（vEsseL sEgmentation using Machine lEarning and coNnecTivity）。该框架由区域生长和机器学习进行的特征提取和像素分类组成。所提出的特征基于灰度级和血管连通性属性捕获互补证据。后者信息在分类阶段无缝传播通过像素。ELEMENT减少了不一致性和加快了分割吞吐量。我们分析并比较了所提出方法与现有血管分割算法在三个主要实验组中的性能，针对每种眼部模态。我们的方法产生了更高的整体性能，整体准确率为97.40%，优于26种现有方法中的25种，包括6种基于深度学习的工作，评估在广泛知名的DRIVE视网膜图像数据集上。在STARE、CHASE-DB、VAMPIRE FA、IOSTAR SLO和RC-SLO数据集中，所提出的框架分别以98.27%、97.78%、98.34%、98.04%和98.35%的准确率超过了所有现有方法。
英文摘要：
Vascular structures in the retina contain important information for the detection and analysis of ocular diseases, including age-related macular degeneration, diabetic retinopathy and glaucoma. Commonly used modalities in diagnosis of these diseases are fundus photography, scanning laser ophthalmoscope (SLO) and fluorescein angiography (FA). Typically, retinal vessel segmentation is carried out either manually or interactively, which makes it time consuming and prone to human errors. In this research, we propose a new multi-modal framework for vessel segmentation called ELEMENT (vEsseL sEgmentation using Machine lEarning and coNnecTivity). This framework consists of feature extraction and pixel-based classification using region growing and machine learning. The proposed features capture complementary evidence based on grey level and vessel connectivity properties. The latter information is seamlessly propagated through the pixels at the classification phase. ELEMENT reduces inconsistencies and speeds up the segmentation throughput. We analyze and compare the performance of the proposed approach against state-of-the-art vessel segmentation algorithms in three major groups of experiments, for each of the ocular modalities. Our method produced higher overall performance, with an overall accuracy of 97.40%, compared to 25 of the 26 state-of-the-art approaches, including six works based on deep learning, evaluated on the widely known DRIVE fundus image dataset. In the case of the STARE, CHASE-DB, VAMPIRE FA, IOSTAR SLO and RC-SLO datasets, the proposed framework outperformed all of the state-of-the-art methods with accuracies of 98.27%, 97.78%, 98.34%, 98.04% and 98.35%, respectively.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
