A mathematical fuzzy fusion framework for whole tumor segmentation in multimodal MRI using Nakagami imaging
The result's identifiers
Result code in IS VaVaI
<a href="https://www.isvavai.cz/riv?ss=detail&h=RIV%2F62690094%3A18450%2F23%3A50019872" target="_blank" >RIV/62690094:18450/23:50019872 - isvavai.cz</a>
Result on the web
<a href="https://www.sciencedirect.com/science/article/pii/S0957417422024812?via%3Dihub" target="_blank" >https://www.sciencedirect.com/science/article/pii/S0957417422024812?via%3Dihub</a>
DOI - Digital Object Identifier
<a href="http://dx.doi.org/10.1016/j.eswa.2022.119462" target="_blank" >10.1016/j.eswa.2022.119462</a>
Alternative languages
Result language
angličtina
Original language name
A mathematical fuzzy fusion framework for whole tumor segmentation in multimodal MRI using Nakagami imaging
Original language description
Automated lesion segmentation has become one of the most important tasks for the experts and researchers dealing with intelligent image processing of brain magnetic resonance images in clinical and bio-medicine. Fluid attenuated inversion recovery (FLAIR) is the universally accepted sequence for highlighting the lesions while suppressing the surrounding cerebrospinal fluid to create utmost contrast; however, in some cases, FLAIR images are not enough to cover the whole lesions in contrast-based segmentation. Therefore, in this paper, we propose a mathematical fuzzy inference-based fusion framework to increase the dice score coefficient (DSC) of the segmentation of FLAIR sequences and to achieve the highest DSC to overcome the inconclusive FLAIR cases. Taken from the BraTS2012 training database, the sample insufficient FLAIR images and the corresponding sequences T1, T1c, and T2 containing high- and low-grade glioma are processed by our fully-mathematical Nakagami imaging method and the lesions are separately segmented and stored. The binary images, generated by the specialized fuzzy c-means segmentation, are fused by a mathematical pixel intensity and distance-based fuzzy inference system to reach the ground truth images with the highest accuracy possible. The average dice score, calculated by segmentation of all images in the BraTS 2012 training database, is computed as 92.78% after fusion of all sequences. As a promising framework, the outputs of this research would be so beneficial for the experts dealing with whole tumor segmentation despite inconclusive FLAIR images. © 2022 Elsevier Ltd
Czech name
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Czech description
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Classification
Type
J<sub>imp</sub> - Article in a specialist periodical, which is included in the Web of Science database
CEP classification
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OECD FORD branch
20201 - Electrical and electronic engineering
Result continuities
Project
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Continuities
I - Institucionalni podpora na dlouhodoby koncepcni rozvoj vyzkumne organizace
Others
Publication year
2023
Confidentiality
S - Úplné a pravdivé údaje o projektu nepodléhají ochraně podle zvláštních právních předpisů
Data specific for result type
Name of the periodical
Expert systems with applications
ISSN
0957-4174
e-ISSN
1873-6793
Volume of the periodical
216
Issue of the periodical within the volume
April
Country of publishing house
GB - UNITED KINGDOM
Number of pages
14
Pages from-to
"Article number: 119462"
UT code for WoS article
000921156300001
EID of the result in the Scopus database
2-s2.0-85145253495