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Retinal Imaging and Image Analysis

Michael D. Abramoff, Mona K. Garvin, Milan Sonka
2010 IEEE Reviews in Biomedical Engineering  
A separate section is devoted to 3-D analysis of OCT images, describing methods for segmentation and analysis of retinal layers, retinal vasculature, and 2-D/3-D detection of symptomatic exudateassociated  ...  This review focuses on quantitative approaches to retinal image analysis. Principles of 2-D and 3-D retinal imaging are outlined first.  ...  for Vision Research, University of Iowa, by the Department of Veterans' Affairs Center of Excellence for the Treatment and Prevention of Visual Loss, and by the Department of Veterans' Affairs Merit program  ... 
doi:10.1109/rbme.2010.2084567 pmid:22275207 pmcid:PMC3131209 fatcat:5tnyvgeujfhovkwlmasuf7huja

Diabetic retinopathy through retinal image analysis: A review

Y. Madhu Sudhana Reddy, R. S. Ernest Ravindran, K. Hari Kishore
2017 International Journal of Engineering & Technology  
The optic disk, blood vessels and exudates gives more analytical details about the retinal image, segmentation of those features are very important.  ...  In this paper, the recent advancement in the Digital Image Processing Aspects in the Diabetic Retinopathy (DR) were been discussed.  ...  Hani [20] proposed an improved nonlinear hue-saturation-intensity color model (INHSI) to preserve color information of the retinal images.  ... 
doi:10.14419/ijet.v7i1.5.9072 fatcat:xzhr5aw4kbb5bbg43rmqbr2fbe

Retinal Image Analysis Using Morphological Process and Clustering Technique

Radha R, Bijee Lakshman
2013 Signal & Image Processing An International Journal  
In this system we use the Probabilistic Neural Network (PNN) for training and testing the pre-processed images.  ...  This paper proposes a method for the Retinal image analysis through efficient detection of exudates and recognizes the retina to be normal or abnormal.  ...  based on feature ranking and neural network.  ... 
doi:10.5121/sipij.2013.4605 fatcat:h57rmkuz7rh6rddux45iaciw3m

Guest Editorial Ophthalmic Image Analysis and Informatics

Jun Cheng, Huazhu Fu, Delia Cabrera DeBuc, Jie Tian
2020 IEEE journal of biomedical and health informatics  
The first network was intended for the synthesis of cataract-like images through unpaired clear retinal images and cataract images.  ...  The model comprises of a densely connected neural network and a recurrent neural network, trainable end-to-end.  ... 
doi:10.1109/jbhi.2020.3037388 fatcat:k2acylvxejf7zg5jw5jq6l7igi

Algorithms for digital image processing in diabetic retinopathy

R.J. Winder, P.J. Morrow, I.N. McRitchie, J.R. Bailie, P.M. Hart
2009 Computerized Medical Imaging and Graphics  
This work examined recent literature on digital image processing in the field of diabetic retinopathy.  ...  Algorithms were categorized into 5 steps (preprocessing; localization and segmentation of the optic disk; segmentation of the retinal vasculature; localization of the macula and fovea; localization and  ...  [70] reported a sensitivity of 93.1% using the previously described approach based upon neural networks. Ege et al.  ... 
doi:10.1016/j.compmedimag.2009.06.003 pmid:19616920 fatcat:uprqflyj3zfupizg5w7ol4jppq

Hyperspectral Image Segmentation of Retinal Vasculature, Optic Disc and Macula

Azat Garifullin, Peeter Koobi, Pasi Ylitepsa, Kati Adjers, Markku Hauta-Kasari, Hannu Uusitalo, Lasse Lensu
2018 2018 Digital Image Computing: Techniques and Applications (DICTA)  
ACKNOWLEDGEMENTS The authors wish to thank CSC for the computational resources for some of the experiments.  ...  Other approaches are based on 3D convolutional neural networks [10] , [11] that can effectively extract both spatial and spectral features.  ...  Furthermore, spectral information can also be utilized for the histological analysis of fundus images [2] .  ... 
doi:10.1109/dicta.2018.8615761 dblp:conf/dicta/GarifullinKYAHU18 fatcat:3xqzoynzwzdytdhyp7gwg6hug4

Disease Prediction Based On Retinal Images using Neural Network Classification

2019 VOLUME-8 ISSUE-10, AUGUST 2019, REGULAR ISSUE  
Therefore, during this project, we are able to implement automatic vessel segmentation approach supported the neural network strategies to offer info regarding blood vessel and vein within the human membrane  ...  Computer-aided analyzed the image of the retina for the diagnostic purpose of the malady.  ...  Therefore, in this project, we can implement an automatic vessel segmentation approach based on the neural network methods to give information about arterial and vein in the human retina.  ... 
doi:10.35940/ijitee.k2491.0981119 fatcat:3azwnfw6bvgupog76o2b5g4t6i

Front Matter: Volume 9784

2016 Medical Imaging 2016: Image Processing  
fetal cortical surface parcellation [9784-18] SESSION 5 IMAGE ENHANCEMENT 9784 0K Geodesic denoising for optical coherence tomography images [9784-19] 9784 0L Combined self-learning based single-image  ...  [9784-21] 9784 0N A novel structured dictionary for fast processing of 3D medical images, with application to computed tomography restoration and denoising [9784-22] SESSION 6 SHAPE 9784 0P Landmark based  ...  [9784-68] 9784 1Y 2D image classification for 3D anatomy localization: employing deep convolutional neural networks [9784-69] 9784 1Z Neural network-based visual body weight estimation for drug  ... 
doi:10.1117/12.2240619 fatcat:kot6cogf4rf6dcjhkzdrr5gahi

A Retinal Vessel Detection Approach Based on Shearlet Transform and Indeterminacy Filtering on Fundus Images

Yanhui Guo, Ümit Budak, Abdulkadir Şengür, Florentin Smarandache
2017 Symmetry  
A fundus image is an effective tool for ophthalmologists studying eye diseases. Retinal vessel detection is a significant task in the identification of retinal disease regions.  ...  A neural network classifier is employed to identify the pixels whose inputs are the features in neutrosophic images.  ...  Acknowledgments: The authors would like to thank the editors and anonymous reviewers for their helpful comments and suggestions.  ... 
doi:10.3390/sym9100235 fatcat:7lkdedbozjgbxewckbpt3virdy

Stroke Prognosis through Retinal Image Analysis

Jeena R S, Sukesh Kumar A
2017 Advances In Image and Video Processing  
The innovations in the field of retinal imaging have paved the way to the development of tools for assisting physicians in stroke prognosis.  ...  This has been compared for various diseases like diabetic retinopathy, hypertensive retinopathy and retinal ischemia against a set of healthy retinal images.  ...  The algorithm performs better for gray images rather than color images.A color image enhancement algorithm based on human visual system based on adaptive filter is proposed by Xinghao Ding et al.  ... 
doi:10.14738/aivp.52.3005 fatcat:6pwwyoigcndc7jppvvqeqsgjie

An automated tracking approach for extraction of retinal vasculature in fundus images

Alireza Osareh, Bita Shadgar
2010 Journal of Ophthalmic & Vision Research  
For every pixel in retinal images, a feature vector was computed utilizing multiscale analysis based on Gabor filters.  ...  The area under the ROC curve reached a value of 0.967. Automated tracking and identification of retinal blood vessels based on Gabor filters and neural network classifiers seems highly successful.  ...  utilizes tracking-based approaches.  ... 
pmid:22737322 pmcid:PMC3380666 fatcat:zrovtxckujd2viyvsoosh7ocj4

CLASSIFICATION OF DIABETIC RETINOPATHY USING IMAGE PROCESSING IN DIABETIC PATIENTS

Madhuri V. Kakade, C. N. Deshmukh
2021 Journal of research in engineering and applied sciences  
For this, we combined a feature extraction approach based on a pre-trained deep neural network model with a machine learning-based support vector machine classification algorithm.  ...  Physicians utilize retinal imaging to detect lesions associated with this illness during eye screening.  ...  A digital image processing-based DR detection approach [12] has been created using retinal images, where the fundus picture is acquired from the patient's retina.  ... 
doi:10.46565/jreas.2021.v06i04.003 fatcat:cf7ncu4fmjbohedeyybyaalcka

Amplification of pixels in medical image data for segmentation via deep learning object-oriented approach

Ahmad Firdaus Ahmad Fadzil, Noor Elaiza Abd Khalid, Shafaf Ibrahim
2021 International Journal of Advanced Technology and Engineering Exploration  
The emergence of machine learning approach for medical image segmentation specifically by employing Convolutional Neural Network (CNN) has become a ubiquity as other approaches does not able to compete  ...  Medical images serve as a very important tool for medical diagnosis. Medical image segmentation is an area of image processing that segments critical parts of a medical image for diagnosis purposes.  ...  employed The dataset employed in this paper is a medical image of retinal fundus image via Digital Retinal Images for Vessel Extraction (DRIVE) database [14] .  ... 
doi:10.19101/ijatee.2020.s1762117 fatcat:eaf3dhfmw5gyrh6s3zfef3gssm

Front Matter: Volume 11318

Thomas M. Deserno, Po-Hao Chen
2020 Medical Imaging 2020: Imaging Informatics for Healthcare, Research, and Applications  
Publication of record for individual papers is online in the SPIE Digital Library. SPIEDigitalLibrary.org Paper Numbering: Proceedings of SPIE follow an e-First publication model.  ...  Utilization of CIDs allows articles to be fully citable as soon as they are published online, and connects the same identifier to all online and print versions of the publication.  ...  CCTA) using deep learning neural networks 11318 13 CT-based pancreatic multi-organ segmentation by a 3D deep attention U-net network 11318 14 Skin cancer segmentation and classification with improved  ... 
doi:10.1117/12.2570206 fatcat:fman4hvttfhhjldpoo2pltdpcm

Retinal Image Processing in Biometrics [chapter]

Rostom Kachouri, Mohamed Akil, Yaroub Elloumi
2019 Series in BioEngineering  
Considered as safe modalities, the retinal vascular network provide a unique pattern for each individual since it does not change throughout the life of the person.  ...  will not be included in this chapter) In this chapter, retinal image processing will be addressed as a Hidden Biometric modality.  ...  [26] used a multi-layered neural network for the detection of retinal blood vessels, and C. Alonso et al. [27] used a faster type of neural networks: Deep Learning (CNN).  ... 
doi:10.1007/978-981-13-0956-4_10 fatcat:xhmewzoomjbnpkaryzhtwy6qum
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