Complexity-Based Analysis in Biomedical Image Analysis: A Review

Kód výsledku v IS VaVaI

<a href="https://www.isvavai.cz/riv?ss=detail&h=RIV%2F29142890%3A_____%2F24%3A00048981" target="_blank" >RIV/29142890:_____/24:00048981 - isvavai.cz</a>

Výsledek na webu

<a href="https://www-worldscientific-com.ezproxy.lib.cas.cz/doi/10.1142/S0218348X24300022" target="_blank" >https://www-worldscientific-com.ezproxy.lib.cas.cz/doi/10.1142/S0218348X24300022</a>

DOI - Digital Object Identifier

<a href="http://dx.doi.org/10.1142/S0218348X24300022" target="_blank" >10.1142/S0218348X24300022</a>

Jazyk výsledku

angličtina

Název v původním jazyce

Complexity-Based Analysis in Biomedical Image Analysis: A Review

Popis výsledku v původním jazyce

This review paper provides an overview of complexity-based analysis techniques in biomedical image analysis, examining their theoretical foundations, computational methodologies, and practical applications across various medical imaging modalities. Through a synthesis of relevant literature, we explore the utility of complexity-based metrics such as fractal dimension, entropy measures, and network analysis in characterizing the complexity of biomedical images (e.g. magnetic resonance imaging (MRI), computed tomography (CT) scans, X-ray images). Additionally, we discuss the clinical implications of complexity-based analysis in areas such as cancer detection, neuroimaging, and cardiovascular imaging, highlighting its potential to improve diagnostic accuracy, prognostic assessment, and treatment outcomes. The review concludes that complexity-based analysis significantly enhances the interpretability and diagnostic power of biomedical imaging, paving the way for more personalized and precise medical care. By elucidating the role of complexity-based analysis in biomedical image analysis, this review aims to provide insights into current trends, challenges, and future directions in this rapidly evolving field.

Název v anglickém jazyce

Complexity-Based Analysis in Biomedical Image Analysis: A Review

Popis výsledku anglicky

This review paper provides an overview of complexity-based analysis techniques in biomedical image analysis, examining their theoretical foundations, computational methodologies, and practical applications across various medical imaging modalities. Through a synthesis of relevant literature, we explore the utility of complexity-based metrics such as fractal dimension, entropy measures, and network analysis in characterizing the complexity of biomedical images (e.g. magnetic resonance imaging (MRI), computed tomography (CT) scans, X-ray images). Additionally, we discuss the clinical implications of complexity-based analysis in areas such as cancer detection, neuroimaging, and cardiovascular imaging, highlighting its potential to improve diagnostic accuracy, prognostic assessment, and treatment outcomes. The review concludes that complexity-based analysis significantly enhances the interpretability and diagnostic power of biomedical imaging, paving the way for more personalized and precise medical care. By elucidating the role of complexity-based analysis in biomedical image analysis, this review aims to provide insights into current trends, challenges, and future directions in this rapidly evolving field.

Druh

J_imp - Článek v periodiku v databázi Web of Science

CEP obor

—

OECD FORD obor

10200 - Computer and information sciences

Projekt

—

Návaznosti

N - Vyzkumna aktivita podporovana z neverejnych zdroju

Rok uplatnění

2024

Kód důvěrnosti údajů

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

Název periodika

Fractals - Complex Geometry Patterns and Scaling in Nature and Society

ISSN

0218-348X

e-ISSN

—

Svazek periodika

32

Číslo periodika v rámci svazku

6

Stát vydavatele periodika

SG - Singapurská republika

Počet stran výsledku

12

Strana od-do

1-12

Kód UT WoS článku

001276315600001

EID výsledku v databázi Scopus

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