Biomedical image data segmentation with using of clustering driven by genetic algorithms
Identifikátory výsledku
Kód výsledku v 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>
Výsledek na webu
<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>
Alternativní jazyky
Jazyk výsledku
angličtina
Název v původním jazyce
Biomedical image data segmentation with using of clustering driven by genetic algorithms
Popis výsledku v původním jazyce
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.
Název v anglickém jazyce
Biomedical image data segmentation with using of clustering driven by genetic algorithms
Popis výsledku anglicky
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.
Klasifikace
Druh
D - Stať ve sborníku
CEP obor
—
OECD FORD obor
20600 - Medical engineering
Návaznosti výsledku
Projekt
<a href="/cs/project/TL01000302" target="_blank" >TL01000302: Vývoj zdravotních prostředků jako efektivní investice pro veřejné i soukromé subjekty</a><br>
Návaznosti
S - Specificky vyzkum na vysokych skolach
Ostatní
Rok uplatnění
2018
Kód důvěrnosti údajů
S - Úplné a pravdivé údaje o projektu nepodléhají ochraně podle zvláštních právních předpisů
Údaje specifické pro druh výsledku
Název statě ve sborníku
Frontiers in Artificial Intelligence and Applications. Volume 303
ISBN
978-1-61499-899-0
ISSN
0922-6389
e-ISSN
—
Počet stran výsledku
7
Strana od-do
101-107
Název nakladatele
IOS Press
Místo vydání
Amsterdam
Místo konání akce
Granada
Datum konání akce
26. 9. 2018
Typ akce podle státní příslušnosti
WRD - Celosvětová akce
Kód UT WoS článku
000467457200008