A Semi-Supervised Learning Approach for Automatic Segmentation of Retinal Lesions Using SURF Blob Detector and Locally Adaptive Binarization

Kód výsledku v IS VaVaI

<a href="https://www.isvavai.cz/riv?ss=detail&h=RIV%2F61989100%3A27240%2F19%3A10242726" target="_blank" >RIV/61989100:27240/19:10242726 - isvavai.cz</a>

Nalezeny alternativní kódy

RIV/62690094:18450/19:50015949 RIV/00843989:_____/19:E0107979

Výsledek na webu

<a href="https://link.springer.com/chapter/10.1007/978-3-030-14802-7_27" target="_blank" >https://link.springer.com/chapter/10.1007/978-3-030-14802-7_27</a>

DOI - Digital Object Identifier

<a href="http://dx.doi.org/10.1007/978-3-030-14802-7_27" target="_blank" >10.1007/978-3-030-14802-7_27</a>

Jazyk výsledku

angličtina

Název v původním jazyce

A Semi-Supervised Learning Approach for Automatic Segmentation of Retinal Lesions Using SURF Blob Detector and Locally Adaptive Binarization

Popis výsledku v původním jazyce

In the clinical ophthalmology, the retinal area is routinely investigated from the retinal images, by the naked eyes. Such subjective assessment may be apparently influenced by ineligible inaccuracies. Therefore, objective assessment of the retinal image records plays an important role for the clinical evaluation and treatment planning. Retinal lesions in premature born children represent one of the most frequent retinal findings which may endanger their vison. These findings are mostly connected with the Retinopathy of Prematurity (RoP). In this paper, we have proposed a novel segmentation model utilizing the SURF blob detector and locally adaptive binarization. The proposed model is able to autonomously detect, and consequently classify retinal lesions. In the result, we obtain a segmentation model of the retinal lesions, where the retinal posterior is effectively separated. As a part of the proposed analysis, we have done objectification and quantitative comparison of the proposed method against some of the state of the art segmentation models by selected evaluating parameters. The proposed method has a potential to be used in the clinical practice as a feedback for the automatic evaluation of the retinal lesions, and also for dynamic retinal lesion&apos;s features extraction. (C) 2019, Springer Nature Switzerland AG.

Název v anglickém jazyce

A Semi-Supervised Learning Approach for Automatic Segmentation of Retinal Lesions Using SURF Blob Detector and Locally Adaptive Binarization

Popis výsledku anglicky

In the clinical ophthalmology, the retinal area is routinely investigated from the retinal images, by the naked eyes. Such subjective assessment may be apparently influenced by ineligible inaccuracies. Therefore, objective assessment of the retinal image records plays an important role for the clinical evaluation and treatment planning. Retinal lesions in premature born children represent one of the most frequent retinal findings which may endanger their vison. These findings are mostly connected with the Retinopathy of Prematurity (RoP). In this paper, we have proposed a novel segmentation model utilizing the SURF blob detector and locally adaptive binarization. The proposed model is able to autonomously detect, and consequently classify retinal lesions. In the result, we obtain a segmentation model of the retinal lesions, where the retinal posterior is effectively separated. As a part of the proposed analysis, we have done objectification and quantitative comparison of the proposed method against some of the state of the art segmentation models by selected evaluating parameters. The proposed method has a potential to be used in the clinical practice as a feedback for the automatic evaluation of the retinal lesions, and also for dynamic retinal lesion&apos;s features extraction. (C) 2019, Springer Nature Switzerland AG.

Druh

D - Stať ve sborníku

CEP obor

—

OECD FORD obor

10201 - Computer sciences, information science, bioinformathics (hardware development to be 2.2, social aspect to be 5.8)

Projekt

Výsledek vznikl pri realizaci vícero projektů. Více informací v záložce Projekty.

Návaznosti

P - Projekt vyzkumu a vyvoje financovany z verejnych zdroju (s odkazem do CEP)

Rok uplatnění

2019

Kód důvěrnosti údajů

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

Název statě ve sborníku

Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics). Volume 11432

ISBN

978-3-030-14801-0

ISSN

0302-9743

e-ISSN

1611-3349

Počet stran výsledku

13

Strana od-do

311-323

Název nakladatele

Springer

Místo vydání

Cham

Místo konání akce

Jogdžakarta

Datum konání akce

8. 4. 2019

Typ akce podle státní příslušnosti

WRD - Celosvětová akce

Kód UT WoS článku

000493319700026