色素上皮脱离检测:影像学技术和算法综述

T. M. Sheeba, S. Albert Antony Raj, M. Anand
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摘要

色素上皮脱落(PED)是视网膜上的一种疾病,当眼睛后部的RPE细胞层分开或撕裂时就会发生。视网膜层以及液体、蛋白质、组织或血管的弯曲是PED疾病的典型特征,最常发生在黄斑。PED会对人的视力造成干扰,通常表现为黑影、视力模糊或部分视力丧失。光学相干断层扫描(OCT)是一种高分辨率和非侵入性的成像方式,可以加速视网膜的结构。OCT无创成像可获得组织的横截面图像体积。本研究的主要目的是对视网膜层分割技术、PED流体分割技术和视网膜OCT图像疾病分类技术进行研究和分类。医疗行业的危重患者越来越多,眼病患者也比目前增加了一倍。人工智能(AI)技术帮助卫生部门进行大量和准确的疾病自动检测。图像分类和模式识别正在利用人工智能技术改变行业。目前正在进行许多研究,利用图像处理来帮助这种疾病的早期诊断。由于人工智能和机器学习的引入,图像处理技术已经取得了进步。本文综述了现有研究中最有效的结构分类方法和图像分割方法。本文综述了近年来适用于机器学习算法在OCT图像中预测视网膜疾病的所有算法。通过对已有研究论文中算法的讨论,为读者识别出最准确的感染眼和正常眼视网膜分类算法,层分割精度高,处理时间短。深入探讨了神经感觉性视网膜脱离相关视网膜下液与视网膜下色素上皮液鉴别的有效方法。本文讨论了多年来用于色素上皮脱离早期诊断的许多算法、结果和成像技术。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Pigment Epithelial Detachment Detection: A Review of Imaging Techniques and Algorithms
Pigment epithelial detachment(PED) is a disorder in retina that happens when RPE layers of cells at the back side of the eye come apart, or get teared. The bend of layers in the retina, as well as fluid, proteins, tissue, or blood vessels, is a defining feature of PED disease, which occurs most frequently in the macula. PED can disturb the vision of the people which is often depict dark shadow, blurry vision or partial loss of vision. The optical coherence tomography (OCT) is a trend set of high resolution and non-invasive imaging modality that expedite the structure of the retina. OCT non-invasively yields cross-sectional volume of images with tissues. The major objective of this research paper is to study, state of art and to classify the retinal layer segmentation techniques, PED fluid segmentation and classification of diseases in retinal OCT images. The medical industry is suffering with more critical patients and the cases are increasing in eye diseases double the number as of now. The artificial intelligence (AI) techniques help the health sector with a great and accurate automatic detection of disease. The image classification and pattern recognition are transforming the industry with artificial intelligence techniques. Many studies are being conducted employing image processing to aid in the early diagnosis of this disease. Image processing techniques have advanced as a result of the introduction of artificial intelligence and machine learning. In this review paper, the structure classification methods and the image segmentation method that are best available existing research is discussed. This review summarizes all the recent algorithms that suits for the application of machine learning algorithms for predicting retinal diseases in OCT images. The algorithms discussed from existing research paper, produce the readers to identify the best accurate algorithm for retinal classification of infected eye and normal eye, precision and less processing time for layer segmentation. The effective methods to differentiate the neurosensory retinal detachment associated sub-retinal fluid from the sub-retinal pigment epithelium fluid are discussed in deep. The many algorithms, outcomes and imaging techniques developed over the years for the early diagnosis of Pigment Epithelial Detachment are discussed in this article.
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