Blood Vessel Segmentation in Video-Sequences From the Human Retina

Kód výsledku v IS VaVaI

<a href="https://www.isvavai.cz/riv?ss=detail&h=RIV%2F00216305%3A26220%2F14%3APU110820" target="_blank" >RIV/00216305:26220/14:PU110820 - isvavai.cz</a>

Výsledek na webu

<a href="http://dx.doi.org/10.1109/IST.2014.6958459" target="_blank" >http://dx.doi.org/10.1109/IST.2014.6958459</a>

DOI - Digital Object Identifier

<a href="http://dx.doi.org/10.1109/IST.2014.6958459" target="_blank" >10.1109/IST.2014.6958459</a>

Jazyk výsledku

angličtina

Název v původním jazyce

Blood Vessel Segmentation in Video-Sequences From the Human Retina

Popis výsledku v původním jazyce

This paper deals with the retinal blood vessel segmentation in fundus video-sequences acquired by experimental fundus video camera. Quality of acquired video-sequences is relatively low and fluctuates across particular frames. Especially, due to the low resolution, poor signal-to-noise ratio, and varying illumination conditions within the frames, application of standard image processing methods might be difficult in such experimental fundus images. In this study, we tried two methods for the segmentation of retinal vessels – matched filtering and Hessian-based approach, originally developed for vessel segmentation in standard fundus images. We showed that modified versions of these two approaches, combined with support vector machine (SVM), can be used also for segmentation in experimental low-quality fundus video-sequences. The SVM classifier trained and consecutively tested on the database of high-resolution images achieved classification accuracy over 94 % and thus revealed a possible applicability of the proposed method on low-quality data. Then, testing on low-quality video-sequences revealed sufficiently large reliability in term of segmentation stability within the sequence with the inter-frame variability in image quality.

Název v anglickém jazyce

Blood Vessel Segmentation in Video-Sequences From the Human Retina

Popis výsledku anglicky

This paper deals with the retinal blood vessel segmentation in fundus video-sequences acquired by experimental fundus video camera. Quality of acquired video-sequences is relatively low and fluctuates across particular frames. Especially, due to the low resolution, poor signal-to-noise ratio, and varying illumination conditions within the frames, application of standard image processing methods might be difficult in such experimental fundus images. In this study, we tried two methods for the segmentation of retinal vessels – matched filtering and Hessian-based approach, originally developed for vessel segmentation in standard fundus images. We showed that modified versions of these two approaches, combined with support vector machine (SVM), can be used also for segmentation in experimental low-quality fundus video-sequences. The SVM classifier trained and consecutively tested on the database of high-resolution images achieved classification accuracy over 94 % and thus revealed a possible applicability of the proposed method on low-quality data. Then, testing on low-quality video-sequences revealed sufficiently large reliability in term of segmentation stability within the sequence with the inter-frame variability in image quality.

Druh

D - Stať ve sborníku

CEP obor

—

OECD FORD obor

20204 - Robotics and automatic control

Projekt

—

Návaznosti

S - Specificky vyzkum na vysokych skolach

Rok uplatnění

2014

Kód důvěrnosti údajů

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

Název statě ve sborníku

2014 IEEE International Conference on Imaging Systems and Techniques (IST) Proceedings

ISBN

978-1-4799-6748-3

ISSN

—

e-ISSN

—

Počet stran výsledku

5

Strana od-do

129-133

Název nakladatele

IEEE

Místo vydání

Santorini, Greece

Místo konání akce

Thira, Santorini, Greece

Datum konání akce

14. 10. 2014

Typ akce podle státní příslušnosti

WRD - Celosvětová akce

Kód UT WoS článku

—