Nakagami-Fuzzy imaging framework for precise lesion segmentation in MRI

Kód výsledku v IS VaVaI

<a href="https://www.isvavai.cz/riv?ss=detail&h=RIV%2F00179906%3A_____%2F22%3A10444561" target="_blank" >RIV/00179906:_____/22:10444561 - isvavai.cz</a>

Nalezeny alternativní kódy

RIV/62690094:18450/22:50019115 RIV/00216208:11150/22:10444561

Výsledek na webu

<a href="https://verso.is.cuni.cz/pub/verso.fpl?fname=obd_publikace_handle&handle=s_yllkv1C_" target="_blank" >https://verso.is.cuni.cz/pub/verso.fpl?fname=obd_publikace_handle&handle=s_yllkv1C_</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>

Jazyk výsledku

angličtina

Název v původním jazyce

Nakagami-Fuzzy imaging framework for precise lesion segmentation in MRI

Popis výsledku v původním jazyce

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 NakagamiFuzzy 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.(c) 2022 Elsevier Ltd. All rights reserved.

Název v anglickém jazyce

Nakagami-Fuzzy imaging framework for precise lesion segmentation in MRI

Popis výsledku anglicky

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 NakagamiFuzzy 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.(c) 2022 Elsevier Ltd. All rights reserved.

Druh

J_imp - Článek v periodiku v databázi Web of Science

CEP obor

—

OECD FORD obor

30103 - Neurosciences (including psychophysiology)

Projekt

<a href="/cs/project/EF18_069%2F0010054" target="_blank" >EF18_069/0010054: IT4Neuro(degeneration)</a><br>

Návaznosti

P - Projekt vyzkumu a vyvoje financovany z verejnych zdroju (s odkazem do CEP)

Rok uplatnění

2022

Kód důvěrnosti údajů

S - Úplné a pravdivé údaje o projektu nepodléhají ochraně podle zvláštních právních předpisů

Název periodika

Pattern Recognition

ISSN

0031-3203

e-ISSN

1873-5142

Svazek periodika

128

Číslo periodika v rámci svazku

AUG

Stát vydavatele periodika

GB - Spojené království Velké Británie a Severního Irska

Počet stran výsledku

17

Strana od-do

108675

Kód UT WoS článku

000793702800003

EID výsledku v databázi Scopus

2-s2.0-85127799544