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Fractal Fract
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Advances in Pattern Recognition—Image and Time Series Analyses—through Fractal Geometry...
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Advances in Pattern Recognition—Image and Time Series Analyses—through Fractal Geometry and Complexity Theory

A special issue of Fractal and Fractional (ISSN 2504-3110).

Deadline for manuscript submissions: 17 January 2025 | Viewed by 3709

Special Issue Editors

Prof. Dr. Lucas C. Ribas

E-Mail Website
Guest Editor
Institute of Biosciences, Humanities and Exact Sciences, São Paulo State University, São José do Rio Preto 15054-000, SP, Brazil
Interests: computer vision; neural networks; complex networks; complex systems; fractal descriptors; pattern recognition
Dr. Leonardo F. S. Scabini

E-Mail Website
Guest Editor
São Carlos Institute of Physics, University of São Paulo, São Carlos 13566-590, SP, Brazil
Interests: computer vision; deep learning; neural networks; complex networks
Prof. Dr. Pier Luigi Gentili

E-Mail Website
Guest Editor
Department of Chemistry, Biology, and Biotechnology, University of Perugia, 06123 Perugia, Italy
Interests: complexity; artificial intelligence; fuzzy logic; photophysics; photochemistry; oscillatory reactions; complex systems; nonlinear dynamics; chaos
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

Recently, methods based on the use of complex systems have been widely used in image analysis and pattern recognition. These methods work to analyze the organization of and interaction between the elements present in the data, such as pixels, superpixels, or objects in images and videos. To undertake such tasks, they consider concepts and approaches to complex systems, such as fractal dimension and descriptors, entropy, deterministic or random walks, complex networks or graphs, cellular automata, among others. These approaches share the ability to describe the irregularity or homogeneity of structures with a high degree of precision. This information is relevant for the functioning of both natural and artificial vision systems, helping in the accurate analysis of images, especially those extracted from nature, medical imaging or non-linear phenomena.

We invite researchers to submit their original work, as well as review articles that discuss recent developments and applications in image analysis and pattern recognition. Submissions should draw on approaches from fractal descriptors and complex systems.

The scope and topics that are invited for submission include (but are not limited to):

  • Fractal dimension and descriptors in image analysis;
  • Fractal in neural networks;
  • Complex systems methods in image processing and analysis;
  • Complex systems methods in machine learning;
  • Complex systems in neural networks and deep learning;
  • Applications of complex systems and fractal methods in biology, medicine, etc.

Prof. Dr. Lucas C. Ribas
Dr. Leonardo F. S. Scabini
Prof. Dr. Pier Luigi Gentili
Guest Editors

Manuscript Submission Information

Manuscripts should be submitted online at www.mdpi.com by registering and logging in to this website. Once you are registered, click here to go to the submission form. Manuscripts can be submitted until the deadline. All submissions that pass pre-check are peer-reviewed. Accepted papers will be published continuously in the journal (as soon as accepted) and will be listed together on the special issue website. Research articles, review articles as well as short communications are invited. For planned papers, a title and short abstract (about 100 words) can be sent to the Editorial Office for announcement on this website.

Submitted manuscripts should not have been published previously, nor be under consideration for publication elsewhere (except conference proceedings papers). All manuscripts are thoroughly refereed through a single-blind peer-review process. A guide for authors and other relevant information for submission of manuscripts is available on the Instructions for Authors page. Fractal and Fractional is an international peer-reviewed open access monthly journal published by MDPI.

Please visit the Instructions for Authors page before submitting a manuscript. The Article Processing Charge (APC) for publication in this open access journal is 2700 CHF (Swiss Francs). Submitted papers should be well formatted and use good English. Authors may use MDPI's English editing service prior to publication or during author revisions.

Keywords

  • complex systems
  • pattern recognition
  • image processing
  • fractal descriptors
  • neural networks
  • network science

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Further information on MDPI's Special Issue polices can be found here.

Published Papers (3 papers)

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16 pages, 3378 KiB  
Article
Multifractal Analysis of Neuronal Morphology in the Human Dorsal Striatum: Age-Related Changes and Spatial Differences
by Zorana Nedeljković, Bojana Krstonošić, Nebojša Milošević, Olivera Stanojlović, Dragan Hrnčić and Nemanja Rajković
Fractal Fract. 2024, 8(9), 514; https://doi.org/10.3390/fractalfract8090514 - 30 Aug 2024
Viewed by 458
Abstract
Multifractal analysis offers a sophisticated method to examine the complex morphology of neurons, which traditionally have been analyzed using monofractal techniques. This study investigates the multifractal properties of two-dimensional neuron projections from the human dorsal striatum, focusing on potential morphological changes related to [...] Read more.
Multifractal analysis offers a sophisticated method to examine the complex morphology of neurons, which traditionally have been analyzed using monofractal techniques. This study investigates the multifractal properties of two-dimensional neuron projections from the human dorsal striatum, focusing on potential morphological changes related to aging and differences based on spatial origin within the nucleus. Using multifractal spectra, we analyzed various parameters, including generalized dimensions and Hölder exponents, to characterize the neurons’ morphology. Despite the detailed analysis, no significant correlation was found between neuronal morphology and age. However, clear morphological differences were observed between neurons from the caudate nucleus and the putamen. Neurons from the putamen displayed higher morphological complexity and greater local homogeneity, while those from the caudate nucleus exhibited more scaling laws and higher local heterogeneity. These findings suggest that while age may not significantly impact neuronal morphology in the dorsal striatum, the spatial origin within this brain region plays a crucial role in determining neuronal structure. Further studies with larger samples are recommended to confirm these findings and to explore the full potential of multifractal analysis in neuronal morphology research. Full article
(This article belongs to the Special Issue Advances in Pattern Recognition—Image and Time Series Analyses—through Fractal Geometry and Complexity Theory)
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41 pages, 10067 KiB  
Article
Estimation of Fractal Dimension and Segmentation of Brain Tumor with Parallel Features Aggregation Network
by Haseeb Sultan, Nadeem Ullah, Jin Seong Hong, Seung Gu Kim, Dong Chan Lee, Seung Yong Jung and Kang Ryoung Park
Fractal Fract. 2024, 8(6), 357; https://doi.org/10.3390/fractalfract8060357 - 14 Jun 2024
Cited by 1 | Viewed by 991
Abstract
The accurate recognition of a brain tumor (BT) is crucial for accurate diagnosis, intervention planning, and the evaluation of post-intervention outcomes. Conventional methods of manually identifying and delineating BTs are inefficient, prone to error, and time-consuming. Subjective methods for BT recognition are biased [...] Read more.
The accurate recognition of a brain tumor (BT) is crucial for accurate diagnosis, intervention planning, and the evaluation of post-intervention outcomes. Conventional methods of manually identifying and delineating BTs are inefficient, prone to error, and time-consuming. Subjective methods for BT recognition are biased because of the diffuse and irregular nature of BTs, along with varying enhancement patterns and the coexistence of different tumor components. Hence, the development of an automated diagnostic system for BTs is vital for mitigating subjective bias and achieving speedy and effective BT segmentation. Recently developed deep learning (DL)-based methods have replaced subjective methods; however, these DL-based methods still have a low performance, showing room for improvement, and are limited to heterogeneous dataset analysis. Herein, we propose a DL-based parallel features aggregation network (PFA-Net) for the robust segmentation of three different regions in a BT scan, and we perform a heterogeneous dataset analysis to validate its generality. The parallel features aggregation (PFA) module exploits the local radiomic contextual spatial features of BTs at low, intermediate, and high levels for different types of tumors and aggregates them in a parallel fashion. To enhance the diagnostic capabilities of the proposed segmentation framework, we introduced the fractal dimension estimation into our system, seamlessly combined as an end-to-end task to gain insights into the complexity and irregularity of structures, thereby characterizing the intricate morphology of BTs. The proposed PFA-Net achieves the Dice scores (DSs) of 87.54%, 93.42%, and 91.02%, for the enhancing tumor region, whole tumor region, and tumor core region, respectively, with the multimodal brain tumor segmentation (BraTS)-2020 open database, surpassing the performance of existing state-of-the-art methods. Additionally, PFA-Net is validated with another open database of brain tumor progression and achieves a DS of 64.58% for heterogeneous dataset analysis, surpassing the performance of existing state-of-the-art methods. Full article
(This article belongs to the Special Issue Advances in Pattern Recognition—Image and Time Series Analyses—through Fractal Geometry and Complexity Theory)
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16 pages, 4164 KiB  
Article
Deep Learning-Based Detection of Human Blastocyst Compartments with Fractal Dimension Estimation
by Muhammad Arsalan, Adnan Haider, Jin Seong Hong, Jung Soo Kim and Kang Ryoung Park
Fractal Fract. 2024, 8(5), 267; https://doi.org/10.3390/fractalfract8050267 - 28 Apr 2024
Cited by 1 | Viewed by 1228
Abstract
In vitro fertilization (IVF) is an efficacious form of aided reproduction to deal with infertility. Human embryos are taken from the body, and these are kept in a supervised laboratory atmosphere during the IVF technique until they exhibit blastocyst properties. A human expert [...] Read more.
In vitro fertilization (IVF) is an efficacious form of aided reproduction to deal with infertility. Human embryos are taken from the body, and these are kept in a supervised laboratory atmosphere during the IVF technique until they exhibit blastocyst properties. A human expert manually analyzes the morphometric properties of the blastocyst and its compartments to predict viability through manual microscopic evaluation. A few deep learning-based approaches deal with this task via semantic segmentation, but they are inaccurate and use expensive architecture. To automatically detect the human blastocyst compartments, we propose a parallel stream fusion network (PSF-Net) that performs the semantic segmentation of embryo microscopic images with inexpensive shallow architecture. The PSF-Net has a shallow architecture that combines the benefits of feature aggregation through depth-wise concatenation and element-wise summation, which helps the network to provide accurate detection using 0.7 million trainable parameters only. In addition, we compute fractal dimension estimation for all compartments of the blastocyst, providing medical experts with significant information regarding the distributional characteristics of blastocyst compartments. An open dataset of microscopic images of the human embryo is used to evaluate the proposed approach. The proposed method also demonstrates promising segmentation performance for all compartments of the blastocyst compared with state-of-the-art methods, achieving a mean Jaccard index (MJI) of 87.69%. The effectiveness of PSF-Net architecture is also confirmed with the ablation studies. Full article
(This article belongs to the Special Issue Advances in Pattern Recognition—Image and Time Series Analyses—through Fractal Geometry and Complexity Theory)
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