Detection and segmentation of retinal lesions in RetCam 3 images based on active contours driven by statisticaly local features
The result's identifiers
Result code in IS VaVaI
<a href="https://www.isvavai.cz/riv?ss=detail&h=RIV%2F00843989%3A_____%2F19%3AE0107863" target="_blank" >RIV/00843989:_____/19:E0107863 - isvavai.cz</a>
Result on the web
<a href="http://advances.utc.sk/index.php/AEEE/article/view/3045/488488572" target="_blank" >http://advances.utc.sk/index.php/AEEE/article/view/3045/488488572</a>
DOI - Digital Object Identifier
<a href="http://dx.doi.org/10.15598/aeee.v17i2.3045" target="_blank" >10.15598/aeee.v17i2.3045</a>
Alternative languages
Result language
angličtina
Original language name
Detection and segmentation of retinal lesions in RetCam 3 images based on active contours driven by statisticaly local features
Original language description
Clinical retinal image analysis is an import aspect of clinical diagnosis in ophthalmology. Retinopathy of Prematurity (ROP) represents one of the most severe retinal disorders in prematurely born infants. One of the ROP clinical signs is the presence of retinal lesions endangering the vision system. Unfortunately, the stage and progress of these findings is often only subjectively estimated. A procedure such as this is undoubtedly linked to subjective inaccuracies depending on the experience of the ophthalmologist. In our study, a fully autonomous segmentation algorithm to model retinal lesions found using RetCam 3 is proposed. The proposed method used a combination of retinal image preprocessing and active contours for retinal lesion segmentation. Based on this procedure, a binary model of retinal lesions that allowed retinal lesions to be classified from a retinal image background was obtained. Another important aspect of the model was feature extraction. These features reliably and automatically described the development stage of an individual lesion. A complex procedure such as this has significant implications for ophthalmic clinical practice in substituting manual clinical procedures and improving the accuracy of routine clinical decisions.
Czech name
—
Czech description
—
Classification
Type
J<sub>imp</sub> - Article in a specialist periodical, which is included in the Web of Science database
CEP classification
—
OECD FORD branch
20601 - Medical engineering
Result continuities
Project
—
Continuities
V - Vyzkumna aktivita podporovana z jinych verejnych zdroju
Others
Publication year
2019
Confidentiality
S - Úplné a pravdivé údaje o projektu nepodléhají ochraně podle zvláštních právních předpisů
Data specific for result type
Name of the periodical
Advances in electrical and electronic engineering
ISSN
1336-1376
e-ISSN
1804-3119
Volume of the periodical
17
Issue of the periodical within the volume
2
Country of publishing house
CZ - CZECH REPUBLIC
Number of pages
8
Pages from-to
194-201
UT code for WoS article
000472599800012
EID of the result in the Scopus database
2-s2.0-85068460064