Detection and segmentation of retinal lesions in RetCam 3 images based on active contours driven by statisticaly local features

Kód výsledku v 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>

Výsledek na webu

<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>

Jazyk výsledku

angličtina

Název v původním jazyce

Detection and segmentation of retinal lesions in RetCam 3 images based on active contours driven by statisticaly local features

Popis výsledku v původním jazyce

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.

Název v anglickém jazyce

Detection and segmentation of retinal lesions in RetCam 3 images based on active contours driven by statisticaly local features

Popis výsledku anglicky

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.

Druh

J_imp - Článek v periodiku v databázi Web of Science

CEP obor

—

OECD FORD obor

20601 - Medical engineering

Projekt

—

Návaznosti

V - Vyzkumna aktivita podporovana z jinych verejnych zdroju

Rok uplatnění

2019

Kód důvěrnosti údajů

S - Úplné a pravdivé údaje o projektu nepodléhají ochraně podle zvláštních právních předpisů

Název periodika

Advances in electrical and electronic engineering

ISSN

1336-1376

e-ISSN

1804-3119

Svazek periodika

17

Číslo periodika v rámci svazku

2

Stát vydavatele periodika

CZ - Česká republika

Počet stran výsledku

8

Strana od-do

194-201

Kód UT WoS článku

000472599800012

EID výsledku v databázi Scopus

2-s2.0-85068460064