{"title":"色素上皮脱离检测:影像学技术和算法综述","authors":"T. M. Sheeba, S. Albert Antony Raj, M. Anand","doi":"10.1109/ICACTA54488.2022.9753607","DOIUrl":null,"url":null,"abstract":"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.","PeriodicalId":345370,"journal":{"name":"2022 International Conference on Advanced Computing Technologies and Applications (ICACTA)","volume":"52 1","pages":"0"},"PeriodicalIF":0.0000,"publicationDate":"2022-03-04","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":"{\"title\":\"Pigment Epithelial Detachment Detection: A Review of Imaging Techniques and Algorithms\",\"authors\":\"T. M. Sheeba, S. Albert Antony Raj, M. Anand\",\"doi\":\"10.1109/ICACTA54488.2022.9753607\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"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.\",\"PeriodicalId\":345370,\"journal\":{\"name\":\"2022 International Conference on Advanced Computing Technologies and Applications (ICACTA)\",\"volume\":\"52 1\",\"pages\":\"0\"},\"PeriodicalIF\":0.0000,\"publicationDate\":\"2022-03-04\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"0\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"2022 International Conference on Advanced Computing Technologies and Applications (ICACTA)\",\"FirstCategoryId\":\"1085\",\"ListUrlMain\":\"https://doi.org/10.1109/ICACTA54488.2022.9753607\",\"RegionNum\":0,\"RegionCategory\":null,\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"\",\"JCRName\":\"\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"2022 International Conference on Advanced Computing Technologies and Applications (ICACTA)","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1109/ICACTA54488.2022.9753607","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
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.