Image-Based Pixel Clustering and Connected Component Labeling in Left Ventricle Segmentation of Cardiac MR Images

Kód výsledku v IS VaVaI

<a href="https://www.isvavai.cz/riv?ss=detail&h=RIV%2F00216305%3A26220%2F15%3APU115946" target="_blank" >RIV/00216305:26220/15:PU115946 - isvavai.cz</a>

Výsledek na webu

<a href="https://ieeexplore.ieee.org/document/7382454" target="_blank" >https://ieeexplore.ieee.org/document/7382454</a>

DOI - Digital Object Identifier

<a href="http://dx.doi.org/10.1109/ICUMT.2015.7382454" target="_blank" >10.1109/ICUMT.2015.7382454</a>

Jazyk výsledku

angličtina

Název v původním jazyce

Image-Based Pixel Clustering and Connected Component Labeling in Left Ventricle Segmentation of Cardiac MR Images

Popis výsledku v původním jazyce

This research demonstrates a completely automated subsecond fast technique for left ventricle (LV) segmentation from clinical cardiac MRI images for the crucial assessment of left ventricular dysfunction as a measure of cardiac diseases. In this work left ventricle segmentation is achieved using the combination of fuzzy c-means which is a pixel based classification method and connected component labeling. This strategic combination obviates user intervention and problem of seed point initialization as it automatically segments the LV accurately on all frames in the complete cardiac cycle in multi-frame MRI. The both methods complement each other such that it achieves sub-second fast computational speed of 0.7 seconds on average per frame. Thus this technique’s computational time for left ventricle segmentation is much faster than iteration based methods. The accuracy of the automatic segmentation technique was tested against manual segmentation on the basis of correlation coefficient. can be considered clinically significant.

Název v anglickém jazyce

Image-Based Pixel Clustering and Connected Component Labeling in Left Ventricle Segmentation of Cardiac MR Images

Popis výsledku anglicky

This research demonstrates a completely automated subsecond fast technique for left ventricle (LV) segmentation from clinical cardiac MRI images for the crucial assessment of left ventricular dysfunction as a measure of cardiac diseases. In this work left ventricle segmentation is achieved using the combination of fuzzy c-means which is a pixel based classification method and connected component labeling. This strategic combination obviates user intervention and problem of seed point initialization as it automatically segments the LV accurately on all frames in the complete cardiac cycle in multi-frame MRI. The both methods complement each other such that it achieves sub-second fast computational speed of 0.7 seconds on average per frame. Thus this technique’s computational time for left ventricle segmentation is much faster than iteration based methods. The accuracy of the automatic segmentation technique was tested against manual segmentation on the basis of correlation coefficient. can be considered clinically significant.

Druh

D - Stať ve sborníku

CEP obor

—

OECD FORD obor

20201 - Electrical and electronic engineering

Projekt

<a href="/cs/project/LO1401" target="_blank" >LO1401: Interdisciplinární výzkum bezdrátových technologií</a><br>

Návaznosti

P - Projekt vyzkumu a vyvoje financovany z verejnych zdroju (s odkazem do CEP)<br>S - Specificky vyzkum na vysokych skolach

Rok uplatnění

2015

Kód důvěrnosti údajů

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

Název statě ve sborníku

2015 7th International Congress on Ultra Modern Telecommunications and Control Systems and Workshops (ICUMT)

ISBN

978-1-4673-9282-2

ISSN

—

e-ISSN

—

Počet stran výsledku

4

Strana od-do

339-342

Název nakladatele

Neuveden

Místo vydání

Brno, Czech Republic

Místo konání akce

Brno

Datum konání akce

6. 10. 2015

Typ akce podle státní příslušnosti

WRD - Celosvětová akce

Kód UT WoS článku

000380551300060