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

Result code in 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>

Alternative codes found

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

Result on the web

<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>

Result language

angličtina

Original language name

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

Original language description

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.

Czech name

—

Czech description

—

Type

D - Article in proceedings

CEP classification

—

OECD FORD branch

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

Project

Result was created during the realization of more than one project. More information in the Projects tab.

Continuities

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

Publication year

2019

Confidentiality

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

Article name in the collection

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

Number of pages

13

Pages from-to

311-323

Publisher name

Springer

Place of publication

Cham

Event location

Jogdžakarta

Event date

Apr 8, 2019

Type of event by nationality

WRD - Celosvětová akce

UT code for WoS article

000493319700026