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Current Automatic Methods for Knee Cartilage Segmentation: A Review

The result's identifiers

  • Result code in 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>

  • Result on the web

    <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>

Alternative languages

  • Result language

    angličtina

  • Original language name

    Current Automatic Methods for Knee Cartilage Segmentation: A Review

  • Original language description

    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.

  • Czech name

  • Czech description

Classification

  • 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)

Result continuities

  • 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)

Others

  • Publication year

    2019

  • Confidentiality

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

Data specific for result type

  • Article name in the collection

    Proceedings - European Workshop on Visual Information Processing, EUVIP 2019

  • ISBN

    978-1-72814-496-2

  • ISSN

    2471-8963

  • e-ISSN

  • Number of pages

    6

  • Pages from-to

    117-122

  • Publisher name

    IEEE

  • Place of publication

    Piscataway

  • Event location

    Řím

  • Event date

    Oct 28, 2019

  • Type of event by nationality

    WRD - Celosvětová akce

  • UT code for WoS article