Modeling and recognition of retinal blood vessels tortuosity in ROP plus disease: A hybrid segmentation - classification scheme
The result's identifiers
Result code in IS VaVaI
<a href="https://www.isvavai.cz/riv?ss=detail&h=RIV%2F00843989%3A_____%2F25%3AE0111657" target="_blank" >RIV/00843989:_____/25:E0111657 - isvavai.cz</a>
Alternative codes found
RIV/61988987:17110/25:A2603DN5 RIV/61989100:27240/25:10257077
Result on the web
<a href="https://onlinelibrary.wiley.com/doi/10.1155/int/6688133" target="_blank" >https://onlinelibrary.wiley.com/doi/10.1155/int/6688133</a>
DOI - Digital Object Identifier
<a href="http://dx.doi.org/10.1155/int/6688133" target="_blank" >10.1155/int/6688133</a>
Alternative languages
Result language
angličtina
Original language name
Modeling and recognition of retinal blood vessels tortuosity in ROP plus disease: A hybrid segmentation - classification scheme
Original language description
Retinopathy of prematurity (ROP) remains a significant cause of childhood blindness despite advancements in neonatal care. Identifying the plus form of ROP, characterized by dilated and tortuous blood vessels, is crucial for timely intervention. This study introduces an intelligent segmentation–classification system for the autonomous detection of retinal blood vessels and the classification of ROP plus form. Utilizing Clarity RetCam 3 images, our system employs morphological image processing and convolutional neural networks (CNNs) for segmentation and classification, respectively. Testing on a dataset of premature infants’ retinal images demonstrates high segmentation accuracy (median = 0.974) and superior classification performance (accuracy = 0.975, sensitivity = 0.950, and specificity = 1). In addition, the system exhibits versatility, with successful segmentation in adult retinal images from public databases. These findings highlight the system’s potential for clinical use in retinal vessel identification, feature extraction, and ROP plus form classification. The proposed system is capable of effectively identifying retinal blood vessels from both alternatives including adult and premature born retinal images with a high accuracy in contrast to related studies. Thus, this system has the potential to be used in clinical practice for retinal blood vessels’ identification, retinal blood vessels’ feature extraction, and ROP plus form classification.
Czech name
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Czech description
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Classification
Type
J<sub>imp</sub> - Article in a specialist periodical, which is included in the Web of Science database
CEP classification
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OECD FORD branch
30207 - Ophthalmology
Result continuities
Project
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Continuities
V - Vyzkumna aktivita podporovana z jinych verejnych zdroju
Others
Publication year
2025
Confidentiality
S - Úplné a pravdivé údaje o projektu nepodléhají ochraně podle zvláštních právních předpisů
Data specific for result type
Name of the periodical
International journal of intelligent systems
ISSN
0884-8173
e-ISSN
1098-111X
Volume of the periodical
2025
Issue of the periodical within the volume
article 6688133
Country of publishing house
GB - UNITED KINGDOM
Number of pages
29
Pages from-to
1-29
UT code for WoS article
001431379800001
EID of the result in the Scopus database
2-s2.0-105000760610