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Automatic analysis of selected choroidal diseases in OCT images of the eye fundus

INTRODUCTION: This paper describes a method for automatic analysis of the choroid in OCT images of the eye fundus in ophthalmology. The problem of vascular lesions occurs e.g. in a large population of patients having diabetes or macular degeneration. Their correct diagnosis and quantitative assessme...

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Autores principales: Koprowski, Robert, Teper, Slawomir, Wróbel, Zygmunt, Wylegala, Edward
Formato: Online Artículo Texto
Lenguaje:English
Publicado: BioMed Central 2013
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC3842656/
https://www.ncbi.nlm.nih.gov/pubmed/24224964
http://dx.doi.org/10.1186/1475-925X-12-117
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author Koprowski, Robert
Teper, Slawomir
Wróbel, Zygmunt
Wylegala, Edward
author_facet Koprowski, Robert
Teper, Slawomir
Wróbel, Zygmunt
Wylegala, Edward
author_sort Koprowski, Robert
collection PubMed
description INTRODUCTION: This paper describes a method for automatic analysis of the choroid in OCT images of the eye fundus in ophthalmology. The problem of vascular lesions occurs e.g. in a large population of patients having diabetes or macular degeneration. Their correct diagnosis and quantitative assessment of the treatment progress are a critical part of the eye fundus diagnosis. MATERIAL AND METHOD: The study analysed about 1’000 OCT images acquired using SOCT Copernicus (Optopol Tech. SA, Zawiercie, Poland). The proposed algorithm for image analysis enabled to analyse the texture of the choroid portion located beneath the RPE (Retinal Pigment Epithelium) layer. The analysis was performed using the profiled algorithm based on morphological analysis and texture analysis and a classifier in the form of decision trees. RESULTS: The location of the centres of gravity of individual objects present in the image beneath the RPE layer proved to be important in the evaluation of different types of images. In addition, the value of the standard deviation and the number of objects in a scene were equally important. These features enabled classification of three different forms of the choroid that were related to retinal pathology: diabetic edema (the classification gave accuracy ACC(1) = 0.73), ischemia of the inner retinal layers (ACC(2) = 0.83) and scarring fibro vascular tissue (ACC(3) = 0.69). For the cut decision tree the results were as follows: ACC(1) = 0.76, ACC(2) = 0.81, ACC(3) = 0.68. CONCLUSIONS: The created decision tree enabled to obtain satisfactory results of the classification of three types of choroidal imaging. In addition, it was shown that for the assumed characteristics and the developed classifier, the location of B-scan does not significantly affect the results. The image analysis method for texture analysis presented in the paper confirmed its usefulness in choroid imaging. Currently the application is further studied in the Clinical Department of Ophthalmology in the District Railway Hospital in Katowice, Medical University of Silesia, Poland.
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spelling pubmed-38426562013-12-06 Automatic analysis of selected choroidal diseases in OCT images of the eye fundus Koprowski, Robert Teper, Slawomir Wróbel, Zygmunt Wylegala, Edward Biomed Eng Online Research INTRODUCTION: This paper describes a method for automatic analysis of the choroid in OCT images of the eye fundus in ophthalmology. The problem of vascular lesions occurs e.g. in a large population of patients having diabetes or macular degeneration. Their correct diagnosis and quantitative assessment of the treatment progress are a critical part of the eye fundus diagnosis. MATERIAL AND METHOD: The study analysed about 1’000 OCT images acquired using SOCT Copernicus (Optopol Tech. SA, Zawiercie, Poland). The proposed algorithm for image analysis enabled to analyse the texture of the choroid portion located beneath the RPE (Retinal Pigment Epithelium) layer. The analysis was performed using the profiled algorithm based on morphological analysis and texture analysis and a classifier in the form of decision trees. RESULTS: The location of the centres of gravity of individual objects present in the image beneath the RPE layer proved to be important in the evaluation of different types of images. In addition, the value of the standard deviation and the number of objects in a scene were equally important. These features enabled classification of three different forms of the choroid that were related to retinal pathology: diabetic edema (the classification gave accuracy ACC(1) = 0.73), ischemia of the inner retinal layers (ACC(2) = 0.83) and scarring fibro vascular tissue (ACC(3) = 0.69). For the cut decision tree the results were as follows: ACC(1) = 0.76, ACC(2) = 0.81, ACC(3) = 0.68. CONCLUSIONS: The created decision tree enabled to obtain satisfactory results of the classification of three types of choroidal imaging. In addition, it was shown that for the assumed characteristics and the developed classifier, the location of B-scan does not significantly affect the results. The image analysis method for texture analysis presented in the paper confirmed its usefulness in choroid imaging. Currently the application is further studied in the Clinical Department of Ophthalmology in the District Railway Hospital in Katowice, Medical University of Silesia, Poland. BioMed Central 2013-11-14 /pmc/articles/PMC3842656/ /pubmed/24224964 http://dx.doi.org/10.1186/1475-925X-12-117 Text en Copyright © 2013 Koprowski et al.; licensee BioMed Central Ltd. http://creativecommons.org/licenses/by/2.0 This is an Open Access article distributed under the terms of the Creative Commons Attribution License (http://creativecommons.org/licenses/by/2.0), which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited. The Creative Commons Public Domain Dedication waiver (http://creativecommons.org/publicdomain/zero/1.0/) applies to the data made available in this article, unless otherwise stated.
spellingShingle Research
Koprowski, Robert
Teper, Slawomir
Wróbel, Zygmunt
Wylegala, Edward
Automatic analysis of selected choroidal diseases in OCT images of the eye fundus
title Automatic analysis of selected choroidal diseases in OCT images of the eye fundus
title_full Automatic analysis of selected choroidal diseases in OCT images of the eye fundus
title_fullStr Automatic analysis of selected choroidal diseases in OCT images of the eye fundus
title_full_unstemmed Automatic analysis of selected choroidal diseases in OCT images of the eye fundus
title_short Automatic analysis of selected choroidal diseases in OCT images of the eye fundus
title_sort automatic analysis of selected choroidal diseases in oct images of the eye fundus
topic Research
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC3842656/
https://www.ncbi.nlm.nih.gov/pubmed/24224964
http://dx.doi.org/10.1186/1475-925X-12-117
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