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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

  • Czech description

Classification

  • Type

    J<sub>imp</sub> - Article in a specialist periodical, which is included in the Web of Science database

  • CEP classification

  • 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