Towards automatic detection of abnormal retinal capillaries in ultra-widefield-of-view retinal angiographic exams

Zutis, Kris; Trucco, Emanuele; Hubschman, Jean‐Pierre; Reed, David; Shah, Sanket U.; Hemert, Jano van

doi:10.1109/embc.2013.6611261

Cited by 4 publications

(3 citation statements)

References 17 publications

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“…It will be important to compare human grading versus computerized automated grading in the future. 8 It is notable that although the absolute total retinal area was significantly greater for single images than summarized images, the percentage of nonperfused retinal area was similar, especially for RVO. It seems that either the summarized image underestimated or the single image overestimated the total retinal area and the non-perfused area proportionally, so that the percentage remained similar in single and summarized image.…”

Section: Discussionmentioning

confidence: 87%

Assessment of Ischemic Index in Retinal Vascular Diseases Using Ultra-Wide-Field Fluorescein Angiography: Single Versus Summarized Image

Franco-Cardenas

Shah

Apap

et al. 2016

Seminars in Ophthalmology

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Section: Discussionmentioning

confidence: 87%

Assessment of Ischemic Index in Retinal Vascular Diseases Using Ultra-Wide-Field Fluorescein Angiography: Single Versus Summarized Image

Franco-Cardenas

Shah

Apap

et al. 2016

Seminars in Ophthalmology

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“…Measures included branching points, vessel length and GLCM textural information. A system using edge contour analysis is presented by Zutis [54] for detecting abnormal retinal capillary regions, with the focus on telangiectasia.…”

mentioning

confidence: 99%

Genetic algorithm based feature selection combined with dual classification for the automated detection of proliferative diabetic retinopathy

Welikala

Fraz

Dehmeshki

et al. 2015

Computerized Medical Imaging and Graphics

147

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Abstract:Proliferative diabetic retinopathy (PDR) is a condition that carries a high risk of severe visual impairment. The hallmark of PDR is the growth of abnormal new vessels. In this paper, an automated method for the detection of new vessels from retinal images is presented. This method is based on a dual classification approach. Two vessel segmentation approaches are applied to create two separate binary vessel map which each hold vital information. Local morphology features are measured from each binary vessel map to produce two separate 4-D feature vectors. Independent classification is performed for each feature vector using a support vector machine (SVM) classifier. The system then combines these individual outcomes to produce a final decision. This is followed by the creation of additional features to generate 21-D feature vectors, which feed into a genetic algorithm based feature selection approach with the objective of finding feature subsets that improve the performance of the classification. Sensitivity and specificity results using a dataset of 60 images are 0.9138 and 0.9600 respectively on a per patch basis and 1.000 and 0.975 respectively on a per image basis.

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“…Zutis [47] presented a system using edge contour analysis for detecting abnormal retinal capillary regions, with the focus on telangiectasia. Doukas [48] created an automated method for the quantification of micro-vessel density within the inner surface of egg shells in order to study the angiogenesis in developing chick embryos.…”

mentioning

confidence: 99%

Automated detection of proliferative diabetic retinopathy using a modified line operator and dual classification

Welikala

Dehmeshki

Hoppe

et al. 2014

Computer Methods and Programs in Biomedicine

107

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address:Roshan.Welikala@kingston.ac.uk (R.A. Welikala). Abstract:Proliferative diabetic retinopathy (PDR) is a condition that carries a high risk of severe visual impairment. The hallmark of PDR is neovascularisation, the growth of abnormal new vessels. This paper describes an automated method for the detection of new vessels in retinal images.Two vessel segmentation approaches are applied, using the standard line operator and a novel modified line operator. The latter is designed to reduce false responses to non-vessel edges.Both generated binary vessel maps hold vital information which must be processed separately. This is achieved with a dual classification system. Local morphology features are measured from each binary vessel map to produce two separate feature sets. Independent classification © 2014, Elsevier. Licensed under the Creative Commons Attribution-NonCommercialNoDerivatives 4.0 International http://creativecommons.org/licenses/by-nc-nd/4.0/ is performed for each feature set using a support vector machine (SVM) classifier. The system then combines these individual classification outcomes to produce a final decision. Sensitivity and specificity results using a dataset of 60 images are 0.862 and 0.944 respectively on a per patch basis and 1.00 and 0.90 respectively on a per image basis.

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Towards automatic detection of abnormal retinal capillaries in ultra-widefield-of-view retinal angiographic exams

Cited by 4 publications

References 17 publications

Assessment of Ischemic Index in Retinal Vascular Diseases Using Ultra-Wide-Field Fluorescein Angiography: Single Versus Summarized Image

Assessment of Ischemic Index in Retinal Vascular Diseases Using Ultra-Wide-Field Fluorescein Angiography: Single Versus Summarized Image

Genetic algorithm based feature selection combined with dual classification for the automated detection of proliferative diabetic retinopathy

Automated detection of proliferative diabetic retinopathy using a modified line operator and dual classification

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