COMPLEXITY-BASED ANALYSIS IN BIOMEDICAL IMAGE ANALYSIS: A REVIEW

Najmeh Pakniyat, Jamaluddin Abdullah, Ondrej Krejcar and Hamidreza Namazi
Additional contact information
Najmeh Pakniyat: 30 Shore Breeze Drive, Toronto, ON, Canada M8V 0J1, Canada
Jamaluddin Abdullah: ��School of Mechanical Engineering, Universiti Sains Malaysia, Nibong Tebal 14300, Penang, Malaysia
Ondrej Krejcar: ��Skoda Auto University, Na Karmeli 1457, Mlada Boleslav 293 01, Czech Republic§Malaysia-Japan International Institute of Technology (MJIIT), Universiti Teknologi Malaysia, Kuala Lumpur 54100, Malaysia¶Media and Games Center of Excellence (MagicX), Universiti Teknologi Malaysia, Johor Bahru 81310, Malaysia
Hamidreza Namazi: ��School of Mechanical Engineering, Universiti Sains Malaysia, Nibong Tebal 14300, Penang, Malaysia‡Skoda Auto University, Na Karmeli 1457, Mlada Boleslav 293 01, Czech Republic

FRACTALS (fractals), 2024, vol. 32, issue 06, 1-12

Abstract: 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.

Keywords: Complexity; Biomedical Image Analysis; Entropy; Fractal Dimension; Network Analysis (search for similar items in EconPapers)
Date: 2024
References: Add references at CitEc
Citations:

Downloads: (external link)
http://www.worldscientific.com/doi/abs/10.1142/S0218348X24300022
Access to full text is restricted to subscribers

Related works:
This item may be available elsewhere in EconPapers: Search for items with the same title.

Export reference: BibTeX RIS (EndNote, ProCite, RefMan) HTML/Text

Persistent link: https://EconPapers.repec.org/RePEc:wsi:fracta:v:32:y:2024:i:06:n:s0218348x24300022

Ordering information: This journal article can be ordered from

DOI: 10.1142/S0218348X24300022

Access Statistics for this article

FRACTALS (fractals) is currently edited by Tara Taylor

More articles in FRACTALS (fractals) from World Scientific Publishing Co. Pte. Ltd.
Bibliographic data for series maintained by Tai Tone Lim ().