Nakagami-Fuzzy imaging framework for precise lesion segmentation in MRI
The result's identifiers
Result code in IS VaVaI
<a href="https://www.isvavai.cz/riv?ss=detail&h=RIV%2F62690094%3A18450%2F22%3A50019115" target="_blank" >RIV/62690094:18450/22:50019115 - isvavai.cz</a>
Alternative codes found
RIV/00216208:11150/22:10444561
Result on the web
<a href="https://www.sciencedirect.com/science/article/pii/S003132032200156X?pes=vor" target="_blank" >https://www.sciencedirect.com/science/article/pii/S003132032200156X?pes=vor</a>
DOI - Digital Object Identifier
<a href="http://dx.doi.org/10.1016/j.patcog.2022.108675" target="_blank" >10.1016/j.patcog.2022.108675</a>
Alternative languages
Result language
angličtina
Original language name
Nakagami-Fuzzy imaging framework for precise lesion segmentation in MRI
Original language description
Nakagami distribution and related imaging methods are very efficient in diagnostic ultrasonography for visualization and characterization of tissues for years. Abnormalities in tissues are distinguished from surrounding cells by application of the distribution ruled by the Nakagami m-parameter. The potential of discrimination in ultrasonography enables intelligent segmentation of lesions by other diagnostic tools and the imaging technique is very promising in other areas of medicine, like magnetic resonance imaging (MRI) for brain lesion identification, as presented in this paper. Therefore, we propose a novel Nakagami-Fuzzy imaging framework for intelligent and fully automated suspicious region segmentation from axial FLAIR MRI images exhibiting brain tumor characteristics to satisfy ground truth images with different precision levels. The images from MRI data set are processed by applying Nakagami distribution from pre-Rayleigh to post-Rayleigh for adjusting m-parameter. Amorphous and non-homogenous suspicious regions revealed by Nakagami imaging are segmented using customized Fuzzy 2-means to compare with two types of binary ground truths. The framework we propose is an outstanding example of fuzzy-based expert systems providing an average of 92.61% dice score for the main clinical experiment we conducted using the images and two types of ground truths provided by University of Hospital, Hradec Kralove. We also tested our framework by the BraTS 2012 and BraTS 2020 datasets and achieved an average of 91.88% and 89.25% dice scores respectively, which are competitive among the relevant researches. © 2022
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
10201 - Computer sciences, information science, bioinformathics (hardware development to be 2.2, social aspect to be 5.8)
Result continuities
Project
<a href="/en/project/EF18_069%2F0010054" target="_blank" >EF18_069/0010054: IT4Neuro(degeneration)</a><br>
Continuities
P - Projekt vyzkumu a vyvoje financovany z verejnych zdroju (s odkazem do CEP)
Others
Publication year
2022
Confidentiality
S - Úplné a pravdivé údaje o projektu nepodléhají ochraně podle zvláštních právních předpisů
Data specific for result type
Name of the periodical
Pattern Recognition
ISSN
0031-3203
e-ISSN
1873-5142
Volume of the periodical
128
Issue of the periodical within the volume
August
Country of publishing house
US - UNITED STATES
Number of pages
17
Pages from-to
"Article number 108675"
UT code for WoS article
000793702800003
EID of the result in the Scopus database
2-s2.0-85127799544