Current Automatic Methods for Knee Cartilage Segmentation: A Review

Kód výsledku v IS VaVaI

<a href="https://www.isvavai.cz/riv?ss=detail&h=RIV%2F61989100%3A27240%2F19%3A10243838" target="_blank" >RIV/61989100:27240/19:10243838 - isvavai.cz</a>

Výsledek na webu

<a href="https://ieeexplore.ieee.org/document/8946132/algorithms#algorithms" target="_blank" >https://ieeexplore.ieee.org/document/8946132/algorithms#algorithms</a>

DOI - Digital Object Identifier

<a href="http://dx.doi.org/10.1109/EUVIP47703.2019.8946132" target="_blank" >10.1109/EUVIP47703.2019.8946132</a>

Jazyk výsledku

angličtina

Název v původním jazyce

Current Automatic Methods for Knee Cartilage Segmentation: A Review

Popis výsledku v původním jazyce

Knee cartilage segmentation has been challenging task for many years. This task is usually connected with two major issues. First object of interest is automatic detection and extraction of knee cartilage shape. Second important issue is detection of osteoarthritis, especially, in early stages. This early deterioration is badly recognizable from native images segmentation significantly contributes to precise localization, detection and extraction of early osteoarthritis. Generally, the knee cartilage automatic segmentation and extraction can be performed by various approaches including edge tracking, intensity-based methods, supervised learning, energy minimization, statistical methods and multiregional segmentation methods. Using of particular segmentation method depends on a compromise which user is willing to accept with respect to robustness, segmentation purpose, computational time, accuracy and level of user interaction. This review is mainly focused on fully automatic segmentation methods bringing the recent informations about modeling of cartilage structure via segmentation approaches. (C) 2019 IEEE.

Název v anglickém jazyce

Current Automatic Methods for Knee Cartilage Segmentation: A Review

Popis výsledku anglicky

Knee cartilage segmentation has been challenging task for many years. This task is usually connected with two major issues. First object of interest is automatic detection and extraction of knee cartilage shape. Second important issue is detection of osteoarthritis, especially, in early stages. This early deterioration is badly recognizable from native images segmentation significantly contributes to precise localization, detection and extraction of early osteoarthritis. Generally, the knee cartilage automatic segmentation and extraction can be performed by various approaches including edge tracking, intensity-based methods, supervised learning, energy minimization, statistical methods and multiregional segmentation methods. Using of particular segmentation method depends on a compromise which user is willing to accept with respect to robustness, segmentation purpose, computational time, accuracy and level of user interaction. This review is mainly focused on fully automatic segmentation methods bringing the recent informations about modeling of cartilage structure via segmentation approaches. (C) 2019 IEEE.

Druh

D - Stať ve sborníku

CEP obor

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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

Proceedings - European Workshop on Visual Information Processing, EUVIP 2019

ISBN

978-1-72814-496-2

ISSN

2471-8963

e-ISSN

—

Počet stran výsledku

6

Strana od-do

117-122

Název nakladatele

IEEE

Místo vydání

Piscataway

Místo konání akce

Řím

Datum konání akce

28. 10. 2019

Typ akce podle státní příslušnosti

WRD - Celosvětová akce

Kód UT WoS článku

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