MULTIFRAC: An ImageJ plugin for multiscale characterization of 2D and 3D stack images

Torre, Iván G.; Heck, Richard J.; Tarquis, A. M.

doi:10.1016/j.softx.2020.100574

Cited by 20 publications

(16 citation statements)

References 30 publications

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“…Figure 8.b reveals a negative correlation between permeability values and the information dimension D 1 with a correlation factor of 0.55. Both figures illustrate that capacity and information dimensions correlate with the porosity and the permeability, respectively, as reported in previous studies [37]- [38], [42]. Indeed, despite significant differences in pore microstructure between the carbonate and sandstone samples, we detect a relationship between D 0 and porosity, indicating that the total porosity is the main factor influencing the capacity dimension D 0 .…”

Section: B Multifractal and Rock Properties Correlationsupporting

confidence: 86%

Multifractal Analysis of Reservoir Rock Samples Using 3D X-Ray Micro Computed Tomography Images

et al. 2022

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Characterizing heterogeneity in reservoir rocks at the pore scale is crucial to understand the flow patterns better, and estimate reservoir petrophysical properties such as porosity and permeability. This study introduces multifractals as descriptors for rock samples' heterogeneity at the pore scale. We analyzed twenty rock samples from sandstone and carbonate reservoirs using their 3D X-ray micro-computed tomography images. In addition, we simulated porosity and permeability properties and examined their correlation with multifractal parameters. The results show that the capacity dimension D 0 and the information dimension D 1 correlate with porosity and permeability simulated from images, respectively. Finally, we calculated several multifractal parameters such as the width of the spectrum, the asymmetry degree of the spectrum in the horizontal direction and the value of the vertical difference between the two branches of the spectrum. Results illustrate the ability of multifractal parameters to classify groups of rock samples according to their degree of heterogeneity.INDEX TERMS Heterogeneity, micro computed tomography, multifractals.

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Section: B Multifractal and Rock Properties Correlationsupporting

confidence: 86%

Multifractal Analysis of Reservoir Rock Samples Using 3D X-Ray Micro Computed Tomography Images

et al. 2022

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“…Second, gray-scale images were evaluated for the fractal dimension using the box-counting method provided by the Multifrac plugin of ImageJ software ( Fig. 1H ) [ 23 ]. Box counting relies on the equation

, where N is the number of objects, ɛ is the linear scaling or magnification factor, and D f is the fractal dimension, which is equivalent to the inverse of the box’s linear dimension ( Supplementary Fig.…”

Section: Methodsmentioning

confidence: 99%

“…1B3). [23] Box-counting relies on the equation 𝑁 ∝ 𝜀 𝐷 𝑓 , where 𝑁 is the number of objects and 𝜀 is the linear scaling or magnification factor, which is equivalent to the inverse of the box's linear dimension (Supplemental Fig. 1).…”

Section: Fractal Dimension Calculationmentioning

confidence: 99%

Fractal Dimension of Peripapillary Vasculature in Primary Open-Angle Glaucoma

Song

Kim

Lee

2022

Korean J Ophthalmol

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To compare the fractal dimensions of the peripapillary microvasculature as obtained by optical coherence tomography angiography (OCT-A) between primary open-angle glaucoma (POAG) and controls.Methods: Optic nerve head (ONH) and peripapillary area images were taken using the 20˚×20˚ scan of Spectralis OCT-A in 97 subjects (64 POAG patients, 33 controls). The ONH microvasculature was evaluated according to pre-defined slabs: 1) the superficial vascular complex (SVC) and 2) the avascular complex (AVC). The en-face image of each slab was processed by ImageJ software in order to calculate the vessel density and the fractal dimension using the box-counting method. For comparison, the peripapillary retinal nerve fiber layer (RNFL) thickness was obtained from Spectralis OCT circle scans. The utilities of the parameters for discriminating between the POAG and control groups were assessed using areas under the receiver operating characteristic (ROC) curves (AUCs). Results:The SVC fractal dimension was lower in the POAG than in the control group (P < 0.001), while AVC showed no intergroup difference (P = 0.563). The fractal dimension showed a good correlation with the vessel density in both SVC and AVC (both P <0.001). In a multivariable regression analysis, the SVC fractal dimension was negatively correlated with age (P < 0.001) and axial length (P < 0.001) and positively correlated with average RNFL thickness (P < 0.001), while the AVC fractal dimension was positively correlated with the Bruch's membrane opening (BMO) size (P = 0.013). In terms of diagnostic utility, the AUC was significantly larger for the average RNFL thickness (AUC = 0.889) than for the SVC fractal dimension (AUC = 0.772, P = 0.008). Conclusions:The fractal dimension of SVC was associated with the average RNFL thickness and was reduced in POAG patients. Fractal dimension analysis could be used in evaluating peripapillary vascularity by OCT-A.

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“…Thus, the space experiment confirmed the effect of electrostatic interaction on the clusters of titania particles, which is difficult to verify on the ground. The fractal dimension of the aggregates was determined from the microscopic images 33,34,35,36,37 . The bulky aggregates with Df = 2 observed in a microgravity environment again indicate the effect of microgravity on the assembly of titania particles.…”

Section: Clusters Of Titania Particlesmentioning

confidence: 99%

Clustering of Charged Colloidal Particles in Microgravity Environment of Space

Yamanaka

Miki

Ishigami

et al. 2022

Preprint

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We conducted a charge-charge clustering experiment of positively and negatively charged colloidal particles in aqueous media under a microgravity environment at the International Space Station. A special setup was used to mix the colloid particles in microgravity and then immobilize these structures in gel cured using UV light. The samples returned to the ground were observed by optical microscopy. The space sample of polystyrene particles with a specific gravity d (=1.05) close to the medium had an average association number of approximately 50% larger than the ground control and better structural symmetry. The effect of electrostatic interactions on the clustering was also confirmed for titania particles (d~3), whose association structures were only possible in the microgravity environment without any sedimentation they generally suffer on the ground. This study suggests that even slight sedimentation and convection on the ground significantly affect the structure formation of colloids. Knowledge from this study will help us to develop a model which will be used to design photonic materials and better drugs.

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MULTIFRAC: An ImageJ plugin for multiscale characterization of 2D and 3D stack images

Cited by 20 publications

References 30 publications

Multifractal Analysis of Reservoir Rock Samples Using 3D X-Ray Micro Computed Tomography Images

Multifractal Analysis of Reservoir Rock Samples Using 3D X-Ray Micro Computed Tomography Images

Fractal Dimension of Peripapillary Vasculature in Primary Open-Angle Glaucoma

Clustering of Charged Colloidal Particles in Microgravity Environment of Space

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