Distribution-based imaging for multiple sclerosis lesion segmentation using specialized fuzzy 2-means powered by Nakagami transmutations

Kód výsledku v IS VaVaI

<a href="https://www.isvavai.cz/riv?ss=detail&h=RIV%2F62690094%3A18450%2F21%3A50018070" target="_blank" >RIV/62690094:18450/21:50018070 - isvavai.cz</a>

Nalezeny alternativní kódy

RIV/00179906:_____/21:10432655

Výsledek na webu

<a href="https://www.sciencedirect.com/science/article/pii/S156849462100404X?via%3Dihub" target="_blank" >https://www.sciencedirect.com/science/article/pii/S156849462100404X?via%3Dihub</a>

DOI - Digital Object Identifier

<a href="http://dx.doi.org/10.1016/j.asoc.2021.107481" target="_blank" >10.1016/j.asoc.2021.107481</a>

Jazyk výsledku

angličtina

Název v původním jazyce

Distribution-based imaging for multiple sclerosis lesion segmentation using specialized fuzzy 2-means powered by Nakagami transmutations

Popis výsledku v původním jazyce

Distribution-based imaging is a promising methodology mainly to differentiate suspicious regions from surrounding tissues by applying a distribution to the images vertically or horizontally, ideally in both directions. The methodology is very useful for contouring and highlighting desired regions even under near-zero contrast conditions; it also leads to flexible segmentation of the lesions by parametric kernels and provides robust results when supported by solid post-segmentation protocols. Given these benefits, what we propose in this research is a specialized fuzzy 2-means algorithm enhanced by parametric distribution-based imaging framework to offer novel solutions for multiple-sclerosis (MS) identification and segmentation from flair MRI images. The interchangeable distributions employed in this research are Rayleigh, Weibull, Gamma, Exponential and Chi-square, which all are mathematically transmuted from Nakagami distribution. The Nakagami m-parameter is defining the shape of the distributions unless a special parameter exists; while the highlighted areas are segmented by fuzzy 2-means. All parameters are optimized using a set of MICCAI 2016 MS lesion segmentation challenge taken by Siemens Verio 3T scanner and 0.8245 dice score is achieved by Nakagami-Gamma. However, when the optimized framework is tested by other 4 sets with same resolution and size properties, the highest average dice score 0.7113 is obtained by Nakagami–Rayleigh; while Nakagami-Gamma transmutation is resulted in 0.7112 dice score with significantly better sensitivity. © 2021 Elsevier B.V.

Název v anglickém jazyce

Distribution-based imaging for multiple sclerosis lesion segmentation using specialized fuzzy 2-means powered by Nakagami transmutations

Popis výsledku anglicky

Distribution-based imaging is a promising methodology mainly to differentiate suspicious regions from surrounding tissues by applying a distribution to the images vertically or horizontally, ideally in both directions. The methodology is very useful for contouring and highlighting desired regions even under near-zero contrast conditions; it also leads to flexible segmentation of the lesions by parametric kernels and provides robust results when supported by solid post-segmentation protocols. Given these benefits, what we propose in this research is a specialized fuzzy 2-means algorithm enhanced by parametric distribution-based imaging framework to offer novel solutions for multiple-sclerosis (MS) identification and segmentation from flair MRI images. The interchangeable distributions employed in this research are Rayleigh, Weibull, Gamma, Exponential and Chi-square, which all are mathematically transmuted from Nakagami distribution. The Nakagami m-parameter is defining the shape of the distributions unless a special parameter exists; while the highlighted areas are segmented by fuzzy 2-means. All parameters are optimized using a set of MICCAI 2016 MS lesion segmentation challenge taken by Siemens Verio 3T scanner and 0.8245 dice score is achieved by Nakagami-Gamma. However, when the optimized framework is tested by other 4 sets with same resolution and size properties, the highest average dice score 0.7113 is obtained by Nakagami–Rayleigh; while Nakagami-Gamma transmutation is resulted in 0.7112 dice score with significantly better sensitivity. © 2021 Elsevier B.V.

Druh

J_imp - Článek v periodiku v databázi Web of Science

CEP obor

—

OECD FORD obor

10201 - Computer sciences, information science, bioinformathics (hardware development to be 2.2, social aspect to be 5.8)

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

2021

Kód důvěrnosti údajů

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

Název periodika

Applied soft computing

ISSN

1568-4946

e-ISSN

—

Svazek periodika

108

Číslo periodika v rámci svazku

September

Stát vydavatele periodika

NL - Nizozemsko

Počet stran výsledku

14

Strana od-do

"Article number 107481"

Kód UT WoS článku

000663564800014

EID výsledku v databázi Scopus

2-s2.0-85105567432