Automated Segmentation and Quantification of Drusen in Fundus and Optical Coherence Tomography Images for Detection of ARMD
Automated Segmentation and Quantification of Drusen in Fundus and Optical Coherence Tomography Images for Detection of ARMD
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DOI：
10.1007/s10278-017-0038-7
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发表时间：
2018-08-01
影响因子：
4.4
通讯作者：
Khalil, Tehmina
中科院分区：
文献类型：
--
作者：
Khalid, Samina;Akram, M. Usman;Khalil, Tehmina
年龄相关性黄斑变性（ARMD）是老年人最常见的视网膜综合征之一。眼底摄影和光学相干断层扫描 (OCT) 等不同的眼部测试技术用于对受 ARMD 影响的患者进行临床检查。许多研究人员致力于从眼底图像中检测ARMD，但很少有人致力于从OCT图像中检测ARMD。然而，只有少数系统能够建立眼底和 OCT 图像之间的对应关系，从而准确预测 ARMD 病理。在本文中，我们提出了全自动决策支持系统，该系统可以通过建立 OCT 和眼底图像之间的对应关系来自动检测 ARMD。该系统还通过将 OCT B 扫描与眼底图像的相应区域相关联来区分早期、可疑和确诊的 ARMD。在第一阶段，所提出的系统使用不同的基于 B 扫描的特征以及支持向量机 (SVM) 来检测玻璃疣的存在并将其分类为 ARMD 或正常情况。如果输入 OCT 扫描被分类为 ARMD，则考虑相应眼底图像的感兴趣区域进行进一步评估。使用对比度增强和自适应阈值对眼底图像进行分析，以从眼底图像中检测可能的玻璃疣，并最终将其分类为早期 ARMD 或晚期 ARMD。所提出的系统在 100 名患者的本地数据集、100 张眼底图像和 6800 个 OCT B 扫描上进行了测试。所提出的系统检测 ARMD 的准确性、灵敏度和特异性等级分别为 98.0、100 和 97.14%。
Age-related macular degeneration (ARMD) is one of the most common retinal syndromes that occurs in elderly people. Different eye testing techniques such as fundus photography and optical coherence tomography (OCT) are used to clinically examine the ARMD-affected patients. Many researchers have worked on detecting ARMD from fundus images, few of them also worked on detecting ARMD from OCT images. However, there are only few systems that establish the correspondence between fundus and OCT images to give an accurate prediction of ARMD pathology. In this paper, we present fully automated decision support system that can automatically detect ARMD by establishing correspondence between OCT and fundus imagery. The proposed system also distinguishes between early, suspect and confirmed ARMD by correlating OCT B-scans with respective region of the fundus image. In first phase, proposed system uses different B-scan based features along with support vector machine (SVM) to detect the presence of drusens and classify it as ARMD or normal case. In case input OCT scan is classified as ARMD, region of interest from corresponding fundus image is considered for further evaluation. The analysis of fundus image is performed using contrast enhancement and adaptive thresholding to detect possible drusens from fundus image and proposed system finally classified it as early stage ARMD or advance stage ARMD. The proposed system is tested on local data set of 100 patients with 100 fundus images and 6800 OCT B-scans. Proposed system detects ARMD with the accuracy, sensitivity, and specificity ratings of 98.0, 100, and 97.14%, respectively.