Segmentation based on gabor transformation with machine learning: Modeling of retinal blood vessels system from retcam images and tortuosity extraction

Result code in IS VaVaI

<a href="https://www.isvavai.cz/riv?ss=detail&h=RIV%2F62690094%3A18450%2F17%3A50014267" target="_blank" >RIV/62690094:18450/17:50014267 - isvavai.cz</a>

Alternative codes found

RIV/61989100:27240/17:10237654

Result on the web

<a href="http://dx.doi.org/10.3233/978-1-61499-800-6-270" target="_blank" >http://dx.doi.org/10.3233/978-1-61499-800-6-270</a>

DOI - Digital Object Identifier

<a href="http://dx.doi.org/10.3233/978-1-61499-800-6-270" target="_blank" >10.3233/978-1-61499-800-6-270</a>

Result language

angličtina

Original language name

Segmentation based on gabor transformation with machine learning: Modeling of retinal blood vessels system from retcam images and tortuosity extraction

Original language description

In a field of the clinical ophthalmology, an analysis of the retinal blood vessels is one of the major assessments in the retinal system. Retinal blood vessels system is clinically imagined either by the fundus camera or retinal probe (RetCam 3 system). The tortuosity is important parameter assessing blood vessel curvature. Unfortunately, this parameter is usually subjectively estimated in the retinal image analysis. The main aim of the analysis is an automatic segmentation with consequent extraction and modelling of the retinal blood vessels system from RetCam 3 in the form of the binary model. Segmentation algorithm utilizes the Gabor wavelet transformation (GT) giving segmentation results for individual parameters setting. Consequent retinal blood vessels classification is carried out on the base of the linear regression with gold standard. The gold standard represents a manually labelled segmentation by the ophthalmologic experts. Binary segmentation model precisely approximates blood vessels area from other structures. This model allows for the tortuosity extraction in a form of the gradient image where each blood vessel element is described by its steepness.

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

<a href="/en/project/GA17-03037S" target="_blank" >GA17-03037S: Investment evaluation of medical device development</a><br>

Continuities

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

Publication year

2017

Confidentiality

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

Article name in the collection

Frontiers in Artificial Intelligence and Applications

ISBN

978-1-61499-799-3

ISSN

0922-6389

e-ISSN

neuvedeno

Number of pages

14

Pages from-to

270-283

Publisher name

IOS Press

Place of publication

Kitakyushu

Event location

Kitakyushu; Japan

Event date

Sep 26, 2017

Type of event by nationality

WRD - Celosvětová akce

UT code for WoS article

—