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COMPLEXITY-BASED ANALYSIS IN BIOMEDICAL IMAGE ANALYSIS: A REVIEW.
- Source :
-
Fractals . 2024, Vol. 32 Issue 6, p1-12. 12p. - Publication Year :
- 2024
-
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. [ABSTRACT FROM AUTHOR]
Details
- Language :
- English
- ISSN :
- 0218348X
- Volume :
- 32
- Issue :
- 6
- Database :
- Academic Search Index
- Journal :
- Fractals
- Publication Type :
- Academic Journal
- Accession number :
- 179869693
- Full Text :
- https://doi.org/10.1142/S0218348X24300022