Fractal Dimensions in the Texture Analysis of MRI Liver Images

The paper deals with the theory of qth-order fractal dimensions and its application to texture analysis. In particular, the state-of-the-art regarding the fractal dimension estimation for characterizing textures is presented. After, the insufficiency of the single fractal dimension is proven and the qth order fractal dimensions are introduced to overcome such drawback. The multifractality spectrum function D(q) is described, a novel algorithm for estimating such dimensions is then proposed, and its use in digital-image processing is addressed. Results on real SAR image textures are reported and discussed.

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Bone Structure Analysis of the Radius Using Ultrahigh Field (7T) MRI: Relevance of Technical Parameters and Comparison with 3T MRI and Radiography

Diagnostics ◽

10.3390/diagnostics11010110 ◽

2021 ◽

Vol 11 (1) ◽

pp. 110

Author(s):

Mohamed Jarraya ◽

Rafael Heiss ◽

Jeffrey Duryea ◽

Armin M. Nagel ◽

John A. Lynch ◽

...

Keyword(s):

High Resolution ◽

Texture Analysis ◽

Patient Group ◽

Structural Changes ◽

Bone Structure ◽

Spin Echo ◽

Fractal Dimensions ◽

Technical Parameters ◽

Significant Difference ◽

Bone Texture Analysis

Bone fractal signature analysis (FSA—also termed bone texture analysis) is a tool that assesses structural changes that may relate to clinical outcomes and functions. Our aim was to compare bone texture analysis of the distal radius in patients and volunteers using radiography and 3T and 7T magnetic resonance imaging (MRI)—a patient group (n = 25) and a volunteer group (n = 25) were included. Participants in the patient group had a history of chronic wrist pain with suspected or confirmed osteoarthritis and/or ligament instability. All participants had 3T and 7T MRI including T1-weighted turbo spin echo (TSE) sequences. The 7T MRI examination included an additional high-resolution (HR) T1 TSE sequence. Radiographs of the wrist were acquired for the patient group. When comparing patients and volunteers (unadjusted for gender and age), we found a statistically significant difference of horizontal and vertical fractal dimensions (FDs) using 7T T1 TSE-HR images in low-resolution mode (horizontal: p = 0.04, vertical: p = 0.01). When comparing radiography to the different MRI sequences, we found a statistically significant difference for low- and high-resolution horizontal FDs between radiography and 3T T1 TSE and 7T T1 TSE-HR. Vertical FDs were significantly different only between radiographs and 3T T1 TSE in the high-resolution mode; FSA measures obtained from 3T and 7T MRI are highly dependent on the sequence and reconstruction resolution used, and thus are not easily comparable between MRI systems and applied sequences.

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Texture Analysis Using Fractal Dimensions

Beiträge zur Graphischen Datenverarbeitung - Fractal Geometry and Computer Graphics ◽

10.1007/978-3-642-95678-2_17 ◽

1992 ◽

pp. 217-230 ◽

Cited By ~ 3

Author(s):

U. Müssigmann

Keyword(s):

Texture Analysis ◽

Fractal Dimensions

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Deep-Analysis of Palmprint Representation Based on Correlation Concept for Human Biometrics Identification

International Journal of Digital Crime and Forensics ◽

10.4018/ijdcf.2020040103 ◽

2020 ◽

Vol 12 (2) ◽

pp. 40-58

Author(s):

Raouia Mokni ◽

Hassen Drira ◽

Monji Kherallah

Keyword(s):

Texture Analysis ◽

Fractal Dimensions ◽

Texture Features ◽

Analysis Method ◽

Frequency Model ◽

Palmprint Identification ◽

Reduction Techniques ◽

Dimensionality Reduction Techniques ◽

Biometrics Identification ◽

Modality Identification

The security of people requires a beefy guarantee in our society, particularly, with the spread of terrorism throughout the world. In this context, palmprint identification based on texture analysis is amongst the pattern recognition applications to recognize people. In this article, the researchers investigated a deep texture analysis for the palmprint texture pattern representation based on a fusion between several texture information extractions through multiple descriptors, such as HOG and Gabor Filters, Fractal dimensions and GLCM corresponding respectively to the frequency, model, and statistical methodologies-based texture features. They assessed the proposed deep texture analysis method as well as the applicability of the dimensionality reduction techniques and the correlation concept between the features-based fusion on the challenging PolyU, CASIA and IIT-Delhi Palmprint databases. The experimental results show that the fusion of different texture types using the correlation concept for palmprint modality identification leads to promising results.

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Fractal dimensions of dynamically triangulated random surfaces

Journal de Physique I ◽

10.1051/jp1:1992275 ◽

1992 ◽

Vol 2 (12) ◽

pp. 2181-2190 ◽

Cited By ~ 4

Author(s):

Christian Münkel ◽

Dieter W. Heermann

Keyword(s):

Fractal Dimensions ◽

Random Surfaces

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Machine Learning Classification of Low-grade and High-grade Chondrosarcomas Based on MRI-based Texture Analysis

10.1055/s-0039-1692575 ◽

2019 ◽

Author(s):

S. Gitto ◽

D. Albano ◽

V. Chianca ◽

R. Cuocolo ◽

L. Ugga ◽

...

Keyword(s):

Machine Learning ◽

Texture Analysis ◽

Low Grade ◽

High Grade ◽

Machine Learning Classification

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Quantitative texture analysis of EUS images accurately differentiates pancreatic cancer from normal tissue and may obviate the need for EUS-FNA

Endoscopy ◽

10.1055/s-2006-947664 ◽

2006 ◽

Vol 39 (S 1) ◽

Author(s):

A Das ◽

F Li ◽

C Nguyen

Keyword(s):

Pancreatic Cancer ◽

Texture Analysis ◽

Normal Tissue

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Structure characterization of ground calcium carbonate flocs by fractal analysis and their effects on handsheet properties

TAPPI Journal ◽

10.32964/tj12.3.17 ◽

2013 ◽

Vol 12 (3) ◽

pp. 17-23 ◽

Cited By ~ 3

Author(s):

WANHEE IM ◽

HAK LAE LEE ◽

HYE JUNG YOUN ◽

DONGIL SEO

Keyword(s):

Fractal Analysis ◽

Structural Characteristics ◽

Fractal Dimensions ◽

Strength Loss ◽

Structure Characterization ◽

Uniform Size ◽

Cross Sectional ◽

Floc Size ◽

Mass Fractal ◽

Handsheet Properties

Preflocculation of filler particles before their addition to pulp stock provides the most viable and practical solution to increase filler content while minimizing strength loss. The characteristics of filler flocs, such as floc size and structure, have a strong influence on preflocculation efficiency. The influence of flocculant systems on the structural characteristics of filler flocs was examined using a mass fractal analysis method. Mass fractal dimensions of filler flocs under high shear conditions were obtained using light diffraction spectroscopy for three different flocculants. A single polymer (C-PAM), a dual cationic polymer (p-DADMAC/C-PAM) and a C-PAM/micropolymer system were used as flocculants, and their effects on handsheet properties were investigated. The C-PAM/micropolymer system gave the greatest improvement in tensile index. The mass fractal analysis showed that this can be attributed to the formation of highly dense and spherical flocs by this flocculant. A cross-sectional analysis of the handsheets showed that filler flocs with more uniform size were formed when a C-PAM/micropolymer was used. The results suggest that a better understanding of the characteristics of preflocculated fillers and their influence on the properties of paper can be gained based on a fractal analysis.

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