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Biomedical image data segmentation with using of clustering driven by genetic algorithms

The result's identifiers

  • Result code in IS VaVaI

    <a href="https://www.isvavai.cz/riv?ss=detail&h=RIV%2F61989100%3A27240%2F18%3A10244410" target="_blank" >RIV/61989100:27240/18:10244410 - isvavai.cz</a>

  • Result on the web

    <a href="http://ebooks.iospress.nl/volumearticle/49923" target="_blank" >http://ebooks.iospress.nl/volumearticle/49923</a>

  • DOI - Digital Object Identifier

    <a href="http://dx.doi.org/10.3233/978-1-61499-900-3-101" target="_blank" >10.3233/978-1-61499-900-3-101</a>

Alternative languages

  • Result language

    angličtina

  • Original language name

    Biomedical image data segmentation with using of clustering driven by genetic algorithms

  • Original language description

    The clustering algorithms, like is the K-means algorithm, are commonly utilized for the biomedical image regional segmentation. One of the major limitations of the clustering algorithms is a definition of the initialization phase. When the initialization distribution of the centroids is improperly set the K-means algorithm is not able to achieve a reliable approximation of the tissues, thus the convergence of such segmentation procedure is significantly limited. Furthermore, when the biomedical image data are corrupted either by the noise, or artefacts, an effectivity of the segmentation is limited as well. We have analyzed a multiregional segmentation model based on the hybrid approach of the K-means algorithm which is driven by the ABC genetic algorithm. We suppose that the initialization distribution of the each cluster&apos;s centroid should reflect minimal variation towards the pixels lying inside the cluster. More the variation is increasing, worse results we obtain. Therefore, we define the fitness function minimizing the inter-cluster variance to obtain an optimal distribution of the image clusters within a predefined number of the ABC algorithm iterations. We have tested the segmentation procedure on a sample of the CT and MR image data, and verified this procedure against standard clustering algorithms. (C) 2018 The authors and IOS Press. All rights reserved.

  • Czech name

  • Czech description

Classification

  • Type

    D - Article in proceedings

  • CEP classification

  • OECD FORD branch

    20600 - Medical engineering

Result continuities

  • Project

    <a href="/en/project/TL01000302" target="_blank" >TL01000302: Medical devices development as an effective investment for public and private entities</a><br>

  • Continuities

    S - Specificky vyzkum na vysokych skolach

Others

  • Publication year

    2018

  • Confidentiality

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

Data specific for result type

  • Article name in the collection

    Frontiers in Artificial Intelligence and Applications. Volume 303

  • ISBN

    978-1-61499-899-0

  • ISSN

    0922-6389

  • e-ISSN

  • Number of pages

    7

  • Pages from-to

    101-107

  • Publisher name

    IOS Press

  • Place of publication

    Amsterdam

  • Event location

    Granada

  • Event date

    Sep 26, 2018

  • Type of event by nationality

    WRD - Celosvětová akce

  • UT code for WoS article

    000467457200008