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'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
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Czech description
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Classification
Type
D - Article in proceedings
CEP classification
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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