This paper describes an approach to lacunarity which adopts the pattern under analysis as the reference for the sliding window procedure. The superiority of such a scheme with respect to more traditional methodologies, especially when dealing with finite-size objects, is established and illustrated through applications to diffusion limited aggregation pattern characterization. It is also shown that, given the enhanced accuracy and sensitivity of this scheme, the shape of the window becomes an important parameter, with advantage for circular windows.
Download full-text PDF |
Source |
|---|---|
| http://dx.doi.org/10.1103/PhysRevE.72.016707 | DOI Listing |
Publication Analysis
Top Keywords
Similar Publications
Parrondo's effects with aperiodic protocols.
Chaos
December 2024
Centro Brasileiro de Pesquisas Físicas, Rio de Janeiro 22290-180, Brazil.
In this work, we study the effectiveness of employing archetypal aperiodic sequencing-namely, Fibonacci, Thue-Morse, and Rudin-Shapiro-on the Parrondian effect. From a capital gain perspective, our results show that these series do yield a Parrondo's paradox with the Thue-Morse based strategy outperforming not only the other two aperiodic strategies but benchmark Parrondian games with random and periodical (AABBAABB…) switching as well. The least performing of the three aperiodic strategies is the Rudin-Shapiro.
View Article and Find Full Text PDFThe Fractal Geometry of the Brain: AnOverview.
Adv Neurobiol
March 2024
Computational NeuroSurgery (CNS) Lab & Macquarie Neurosurgery, Macquarie Medical School, Faculty of Medicine, Human and Health Sciences, Macquarie University, Sydney, NSW, Australia.
The first chapter of this book introduces some history, philosophy, and basic concepts of fractal geometry and discusses how the neurosciences can benefit from applying computational fractal-based analysis. Further, it compares fractal with Euclidean approaches to analyzing and quantifying the brain in its entire physiopathological spectrum and presents an overview of the first section of this book as well.
View Article and Find Full Text PDFThree-Dimensional Fractal Analysis of the Interstitial Cells of Cajal Networks of Gastrointestinal Tissue Specimens.
Cell Mol Bioeng
February 2024
Auckland Bioengineering Institute, The University of Auckland, Auckland, New Zealand.
Introduction: Several functional gastrointestinal disorders (FGIDs) have been associated with the degradation or remodeling of the network of interstitial cells of Cajal (ICC). Introducing fractal analysis to the field of gastroenterology as a promising data analytics approach to extract key structural characteristics that may provide insightful features for machine learning applications in disease diagnostics. Fractal geometry has advantages over several physically based parameters (or classical metrics) for analysis of intricate and complex microstructures that could be applied to ICC networks.
View Article and Find Full Text PDFNon-invasive quantification of corneal vascularization using anterior segment optical coherence tomography angiography.
Sci Rep
January 2024
Department of Ophthalmology and Optometry, Medical University of Vienna, Spitalgasse 23, 1090, Vienna, Austria.
Article Synopsis
- Corneal vascularization (CV) can compromise the cornea's ability to heal and increase the risk of rejection after corneal transplants (keratoplasty), making reliable imaging methods important for pre-surgical evaluation.
- This study assessed the effectiveness of Anterior Segment Optical Coherence Tomography Angiography (AS-OCTA) in visualizing and quantifying CV compared to traditional slit-lamp photography in high-risk eyes.
- Findings indicated that AS-OCTA provided clearer and more detailed images of vascular structures and metrics related to CV, suggesting it could be an essential tool for monitoring and guiding treatment before keratoplasty.
Classification of Multiple H&E Images via an Ensemble Computational Scheme.
Entropy (Basel)
December 2023
Department of Computer Science and Statistics (DCCE), São Paulo State University (UNESP), Rua Cristóvão Colombo, 2265, São José do Rio Preto 15054-000, SP, Brazil.
In this work, a computational scheme is proposed to identify the main combinations of handcrafted descriptors and deep-learned features capable of classifying histological images stained with hematoxylin and eosin. The handcrafted descriptors were those representatives of multiscale and multidimensional fractal techniques (fractal dimension, lacunarity and percolation) applied to quantify the histological images with the corresponding representations via explainable artificial intelligence (xAI) approaches. The deep-learned features were obtained from different convolutional neural networks (DenseNet-121, EfficientNet-b2, Inception-V3, ResNet-50 and VGG-19).
View Article and Find Full Text PDFWant AI Summaries of new PubMed Abstracts delivered to your In-box?
Enter search terms and have AI summaries delivered each week - change queries or unsubscribe any time!