Diagnostic Performance of Muscle Echo Intensity and Fractal Dimension for the Detection of Frailty Phenotype

2021 ◽  
pp. 016173462110296
Author(s):  
Rebeca Mirón Mombiela ◽  
Jelena Vucetic ◽  
Paloma Monllor ◽  
Jenny S. Cárdenas-Herrán ◽  
Paloma Taltavull de La Paz ◽  
...  
Keyword(s):  
Fractal Dimension ◽  
Diagnostic Performance ◽  
Region Of Interest ◽  
Rectus Femoris ◽  
Echo Intensity ◽  
Frailty Phenotype ◽  
Box Counting ◽  
Scatter Plots ◽  
The Relationship ◽  
Ultrasound Parameters

To determine the relationship between muscle echo intensity (EI) and fractal dimension (FD), and the diagnostic performance of both ultrasound parameters for the identification of frailty phenotype. A retrospective interpretation of ultrasound scans from a previous cohort (November 2014–February 2015) was performed. The sample included healthy participants <60 years old, and participants ≥60 divided into robust, pre-frail, and frail groups according to Fried frailty criteria. A region of interest of the rectus femoris from the ultrasound scan was segmented, and histogram function was applied to obtain EI. For fractal analysis, images were processed using two-dimensional box-counting techniques to calculate FD. Statistical analyses were performed with diagnostic performance tests. A total of 102 participants (mean age 63 ± 16, 57 men) were evaluated. Muscle fractal dimension correlated with EI ( r = .38, p < .01) and showed different pattern in the scatter plots when participants were grouped by non-frail (control + robust) and frail (pre-frail + frail). The diagnostic accuracy for EI to categorize frailty was of 0.69 (95%CI: 0.59–0.78, p = .001), with high intra-rater (ICC: 0.98, 95%CI: 0.98–0.99); p < .001) and inter-rater (ICC: 0.89, 95%CI: 0.75–0.95; p < .001) reliability and low measurement error for both parameters (EI: −0.18, LOA95%: −10.8 to 10.5; FD: 0.00, LOA95%: −0.09 to 0.10) in arbitrary units. The ROC curve combining both parameters was not better than EI alone ( p = .18). Muscle FD correlated with EI and showed different patterns according to frailty phenotype, with EI outperforming FD as a possible diagnostic tool for frailty.

scholarly journals Ultrasonography-Based Qualitative and Quantitative Evaluation Approaches for Pompe Disease

2019 ◽  
Vol 40 (2) ◽  
pp. 189-203
Author(s):  
Yueh-Hui Lee ◽  
Hong-Jen Chiou ◽  
Da-Tian Bau ◽  
Dau-Ming Niu ◽  
Ting-Rong Hsu ◽  
...  
Keyword(s):  
Pompe Disease ◽  
Clinical Symptoms ◽  
Subcutaneous Fat ◽  
Region Of Interest ◽  
Rectus Femoris ◽  
Normal Subjects ◽  
Control Group ◽  
Ultrasound Images ◽  
Echo Intensity ◽  
Qualitative And Quantitative

Abstract Purpose The aim of this study was to propose the qualitative and quantitative approaches to evaluate the skeletal muscle ultrasound images of 23 Pompe disease (i.e., acid maltase deficiency, AMD) patients and 14 normal subjects. Methods A cohort of 23 AMD patients and 14 normal subjects has been investigated. We compared the B-mode echo intensity of the rectus femoris muscle with that of its surrounding fat (subcutaneous fat) and proposed a qualitative grading method. Quantitative analysis of the region of interest (ROI) with the echo intensity and the segmented area was also performed. Results Qualitative results showed that AMD patients without clinical symptoms (without undergoing ERT) had the highest distribution of Grade 1, and AMD patients undergoing ERT had the widest distribution of Grade 2, and control group (n = 14) with the highest distribution of Grade 1. Using the segmented area approach, quantitative results showed that AMD patients undergoing ERT had the largest and widest distribution. Meanwhile the control subjects (normal subjects) had the lowest and the narrowest areas. The echo intensity of the segmented ROI of AMD patients undergoing ERT displayed the highest and widest (inhomogeneous) distributions. By contrast, the echo intensity of AMD patients without clinical symptoms was slightly increased and with low inhomogeneity. Conclusion The proposed ultrasonography-based qualitative and quantitative approach may be used to evaluate the severity of muscle destruction for AMD patients. Besides, the quantitative segmented area with regression analysis could help predict the incidence of onset of Pompe disease patients.

scholarly journals Classification of Building Images using Fractal Features

2021 ◽  
Vol 9 (5) ◽  
pp. 183-185
Author(s):  
A. Sangeetha ◽  
R. Rajakumari
Keyword(s):  
Fractal Dimension ◽  
Region Of Interest ◽  
Machine Learning Algorithms ◽  
Complex Objects ◽  
Box Counting ◽  
Efficient Measure ◽  
Sobel Edge Detection ◽  
Edge Detection Method ◽  
Extent Of Damage

Cracks in concrete buildings may show the total extent of damage or problems of greater magnitude. Causes of cracks depend on the nature of the crack and the type of structure. Crack classification is an approach to using machine learning algorithms to find a particular type of crack. The image is preprocessed by image smoothening and removes noise using a Gaussian filter, whereas the Sobel edge detection method is used to detect the edges. By using k-means clustering, the image segmentation is carried out to identify the Region of Interest. Fractal dimension is an efficient measure for complex objects. Fractal features like fractal dimension, average, and lacunarity are calculated using a differential box-counting algorithm. The classification of the crack classifies the crack based on the characteristics derived from the crack area.

3D box-counting algorithm for calculating fractal dimension of cities

2010 ◽  
Vol 30 (8) ◽  
pp. 2070-2072
Author(s):  
Le-shan ZHANG ◽  
Ge CHEN ◽  
Yong HAN ◽  
Tao ZHANG
Keyword(s):  
Fractal Dimension ◽  
Box Counting ◽  
Counting Algorithm

scholarly journals Supramolecular Fractal Growth of Self-Assembled Fibrillar Networks

Gels ◽  
2021 ◽  
Vol 7 (2) ◽  
pp. 46
Author(s):  
Pedram Nasr ◽  
Hannah Leung ◽  
France-Isabelle Auzanneau ◽  
Michael A. Rogers
Keyword(s):  
Fractal Dimension ◽  
Crystallization Temperature ◽  
Cayley Tree ◽  
Fractal Dimensions ◽  
Self Similarity ◽  
Avrami Model ◽  
Cluster Aggregation ◽  
Box Counting ◽  
Self Assembled ◽  
Limited Diffusion

Complex morphologies, as is the case in self-assembled fibrillar networks (SAFiNs) of 1,3:2,4-Dibenzylidene sorbitol (DBS), are often characterized by their Fractal dimension and not Euclidean. Self-similarity presents for DBS-polyethylene glycol (PEG) SAFiNs in the Cayley Tree branching pattern, similar box-counting fractal dimensions across length scales, and fractals derived from the Avrami model. Irrespective of the crystallization temperature, fractal values corresponded to limited diffusion aggregation and not ballistic particle–cluster aggregation. Additionally, the fractal dimension of the SAFiN was affected more by changes in solvent viscosity (e.g., PEG200 compared to PEG600) than crystallization temperature. Most surprising was the evidence of Cayley branching not only for the radial fibers within the spherulitic but also on the fiber surfaces.

CT Image Analysis on the Meso-Damage of Construction Waste Recycled Brick

2012 ◽  
Vol 588-589 ◽  
pp. 1930-1933
Author(s):  
Guo Song Han ◽  
Hai Yan Yang ◽  
Xin Pei Jiang
Keyword(s):  
Fractal Dimension ◽  
Strain Curve ◽  
Ct Image ◽  
Stress Strain Curve ◽  
Crack Evolution ◽  
Construction Waste ◽  
Box Counting ◽  
Industrial Ct ◽  
Ct Image Analysis ◽  
Box Counting Method

Based on industrial CT technique, Meso-mechanical experiment was conducted on construction waste recycled brick to get the real-time CT image and stress-strain curve of brick during the loading process. Box counting method was used to calculate the fractal dimension of the inner pore transfixion and crack evolution. The results showed that lots of pore in the interfacial transition zone mainly resulted in the damage of the brick. With the increase of stress, the opening through-pore appeared and crack expanded, and the fractal dimension increased.

Outer Cutoff Value for the Box-Counting Method for Fractal Analysis of the Nucleus Using Kirsch Edge Detection

2020 ◽  
pp. 1-8
Author(s):  
Haruhiko Yoshioka ◽  
Kouki Minami ◽  
Hirokazu Odashima ◽  
Keita Miyakawa ◽  
Kayo Horie ◽  
...  
Keyword(s):  
Fractal Dimension ◽  
Edge Detection ◽  
Fractal Analysis ◽  
Nuclear Size ◽  
Original Position ◽  
Cytological Diagnosis ◽  
Cutoff Value ◽  
Position Bias ◽  
Box Counting ◽  
Nuclear Rotation

<b><i>Objective:</i></b> The complexity of chromatin (i.e., irregular geometry and distribution) is one of the important factors considered in the cytological diagnosis of cancer. Fractal analysis with Kirsch edge detection is a known technique to detect irregular geometry and distribution in an image. We examined the outer cutoff value for the box-counting (BC) method for fractal analysis of the complexity of chromatin using Kirsch edge detection. <b><i>Materials:</i></b> The following images were used for the analysis: (1) image of the nucleus for Kirsch edge detection measuring 97 × 122 pix (10.7 × 13.4 μm) with a Feret diameter of chromatin mesh (<i>n</i> = 50) measuring 17.3 ± 1.8 pix (1.9 ± 0.5 μm) and chromatin network distance (<i>n</i> = 50) measuring 4.4 ± 1.6 pix (0.49 ± 0.18 μm), and (2) sample images for Kirsch edge detection with varying diameters (10.4, 15.9, and 18.1 μm) and network width of 0.4 μm. <b><i>Methods:</i></b> Three types of bias that can affect the outcomes of fractal analysis in cytological diagnosis were defined. (1) Nuclear position bias: images of 9 different positions generated by shifting the original position of the nucleus in the middle of a 256 × 256 pix (28.1 μm) square frame in 8 compass directions. (2) Nuclear rotation bias: images of 8 different rotations obtained by rotating the original position of the nucleus in 45° increments (0°, 45°, 90°, 135°, 180°, 225°, 270°, and 315°). (3) Nuclear size bias: images of varying size (diameter: 190 pix [10.4 μm], 290 pix [15.9 μm], and 330 pix [18.1 μm]) with the same mesh pattern (network width: 8 pix [0.4 μm]) within a 512 × 512 pix square. Different outer cutoff values for the BC method (256, 128, 64, 32, 16, and 8 pix) were applied for each bias to assess the fractal dimension and to compare the coefficient of variation (CV). <b><i>Results:</i></b> The BC method with the outer cutoff value of 32 pix resulted in the least variation of fractal dimension. Specifically, with the cutoff value of 32 pix, the CV of nuclear position bias, nuclear rotation bias, and nuclear size bias were &#x3c;1% (0.1, 0.4, and 0.3%, respectively), with no significant difference between the position and rotation bias (<i>p</i> = 0.19). Our study suggests that the BC method with the outer cutoff value of 32 pix is suitable for the analysis of the complexity of chromatin with chromatin mesh.

Fractal and Convolutional Analysis for Deep Atmospheric Turbulence Using Machine Learning

2021 ◽  
Author(s):  
Nicholas Dudu ◽  
Arturo Rodriguez ◽  
Gael Moran ◽  
Jose Terrazas ◽  
Richard Adansi ◽  
...  
Keyword(s):  
Fractal Dimension ◽  
Atmospheric Turbulence ◽  
Isotropic Turbulence ◽  
Passive Scalar ◽  
Counting Method ◽  
Data Set ◽  
Box Counting ◽  
Box Counting Dimension ◽  
Self Similar ◽  
Box Counting Method

Abstract Atmospheric turbulence studies indicate the presence of self-similar scaling structures over a range of scales from the inertial outer scale to the dissipative inner scale. A measure of this self-similar structure has been obtained by computing the fractal dimension of images visualizing the turbulence using the widely used box-counting method. If applied blindly, the box-counting method can lead to misleading results in which the edges of the scaling range, corresponding to the upper and lower length scales referred to above are incorporated in an incorrect way. Furthermore, certain structures arising in turbulent flows that are not self-similar can deliver spurious contributions to the box-counting dimension. An appropriately trained Convolutional Neural Network can take account of both the above features in an appropriate way, using as inputs more detailed information than just the number of boxes covering the putative fractal set. To give a particular example, how the shape of clusters of covering boxes covering the object changes with box size could be analyzed. We will create a data set of decaying isotropic turbulence scenarios for atmospheric turbulence using Large-Eddy Simulations (LES) and analyze characteristic structures arising from these. These could include contours of velocity magnitude, as well as of levels of a passive scalar introduced into the simulated flows. We will then identify features of the structures that can be used to train the networks to obtain the most appropriate fractal dimension describing the scaling range, even when this range is of limited extent, down to a minimum of one order of magnitude.

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