Box-Counting Method of 2D Neuronal Image: Method Modification and Quantitative Analysis Demonstrated on Images from the Monkey and Human Brain

Rajković, Nemanja; Krstonošić, Bojana; Milošević, Nebojša T.

doi:10.1155/2017/8967902

Cited by 23 publications

(17 citation statements)

References 24 publications

(73 reference statements)

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“…The fractal analysis of the retinal vascular network was performed using the box-counting method (Smith et al, 1996 ; Fernández and Jelinek, 2001 ; Milošević, 2015 , 2016 ; Rajkovic et al, 2017 ). The RFI images were imported in Image J (National Institutes of Health, Bethesda, MD) and used to calculate the FD after grayscale format conversion (Figure 1 ).…”

Section: Methodsmentioning

confidence: 99%

Investigating the Fractal Dimension of the Foveal Microvasculature in Relation to the Morphology of the Foveal Avascular Zone and to the Macular Circulation in Patients With Type 2 Diabetes Mellitus

et al. 2018

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In this study, we examined the relationship between the fractal dimension (FD), the morphology of the foveal avascular zone (FAZ) and the macular circulation in healthy controls and patients with type 2 diabetes mellitus (T2DM) with and with no diabetic retinopathy (DR). Cross-sectional data of 47 subjects were analyzed from a 5-year longitudinal study using a multimodal optical imaging approach. Healthy eyes from nondiabetic volunteers (n = 12) were selected as controls. Eyes from patients with T2DM were selected and divided into two groups: diabetic subjects with mild DR (MDR group, n = 15) and subjects with DM but without DR (DM group, n = 20). Our results demonstrated a higher FD in the healthy group (mean, 1.42 ± 0.03) than in the DM and MDR groups (1.39 ± 0.02 and 1.35 ± 0.03, respectively). Also, a bigger perimeter, area, and roundness of the FAZ were found in MDR eyes. A significant difference in area and perimeter (p ≤ 0.005) was observed for the MDR group supporting the enlargement of the FAZ due to diabetic complications in the eye. A moderate positive correlation (p = 0.014, R2 = 43.8%) between the FD and blood flow rate (BFR) was only found in the healthy control group. The BFR calculations revealed the lowest values in the MDR group (0.98 ± 0.27 μl/s vs. 1.36 ± 0.86 μl/s and 1.36 ± 0.57 μl/sec in the MDR, DM, and healthy groups, respectively, p = 0.2). Our study suggests that the FD of the foveal vessel arborization could provide useful information to identify early morphological changes in the retina of patients with T2DM. Our results also indicate that the enlargement and asymmetry of the FAZ might be related to a lower BFR because of the DR onset and progression. Interestingly, due to the lack of FAZ symmetry observed in the DM and MDR eyes, it appears that the distribution of flow within the retinal vessels loses complexity as the vascular structures distributing the flow are not well described by fractal branching. Further research could determine how our approach may be used to aid the diagnosis of retinal neurodegeneration and vascular impairment at the early stage of DR.

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Section: Methodsmentioning

confidence: 99%

Investigating the Fractal Dimension of the Foveal Microvasculature in Relation to the Morphology of the Foveal Avascular Zone and to the Macular Circulation in Patients With Type 2 Diabetes Mellitus

et al. 2018

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“…Then, we investigated whether there is a connection between two parameters of the monofractal [13] and the GLCM analysis [15]. We have shown that even though these parameters describe different image properties, there is a correlation between them (Figure 2).…”

Section: Computational Analysismentioning

confidence: 99%

“…Among various methods of computational analysis, the monofractal analysis appears to be the method which suitably computes natural objects, when they can be characterized with the unique value of the fractal dimension [13,19]. On the contrary, when objects possess an uneven distribution of complexity, with fractal dimension fluctuating from point to point within an object [20], then a multifractal analysis is appropriate.…”

Section: Quantification Of the Binary Imagementioning

confidence: 99%

“…The binary image represents compression of an initial image to two values (typically in 0 and 255). The outline image was created when one-pixel wide outline in a binary image was generated [13]. There is another type of image known as skeleton image of the object [21].…”

Section: Quantification Of the Binary Imagementioning

confidence: 99%

“…To achieve this goal, four fractal and five textural parameters were used. Although two fractal parameters have been used previously [11,12], we utilized a novel and improved method of calculation [13]. Moreover, grayscale imaging was used to analyze five neuronal properties for the first time.…”

Section: Introductionmentioning

confidence: 99%

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Two Dimensional Grayscale Images of the Aspiny Neurons from the Human Neostriatum: Monofractal and Gray Level Co-occurrence Matrix Analysis

Vranes¹,

Krstonošić²,

Milošević³

2019

EJB

Self Cite

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The striatum (neostriatum) is one of the principal constituents of the basal nuclei. It is a complex structure which consists of a dorsal and the ventral components. According to the spine distribution and their density, neurons of the human striatum can be classified into two main types: spiny and aspiny cells. Further classification recognizes two groups of spiny, and three groups of aspiny neurons. The goal of this study was to analyze different morphometric properties of the digital images of the group IV and group V aspiny neurons, from the dorsal striatum of both cerebral hemispheres. In this study, a total of 175 two-dimensional images of aspiny neurons were analyzed. Image reconstruction and measurement was performed with the specialized, public software Image J. Four parameters of standard fractal analysis were quantified from these binary images. In addition, five textural parameters were obtained by analyzing grayscale images of the entire neuron. Results of both analyses show that six of nine parameters differed between the group IV and group V aspiny neurons. Moreover, in both groups of neurons, one parameter of the fractal and three of the texture analyses differed between the putamen and the caudate nucleus neurons. Thus, this study corroborates previous classification of aspiny neurons. Although they belong to the same aspiny group, different type of cells can qualify nerve signals in their own way. Therefore, this study supports the hypothesis that neuronal morphology differences can reflect their functional diversity and their role in communication.

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Morphology and Fractal-Based Classifications of Neurons and Microglia in Two and Three Dimensions

Karperien,

Jelinek

2024

Advances in Neurobiology

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Box-Counting Method of 2D Neuronal Image: Method Modification and Quantitative Analysis Demonstrated on Images from the Monkey and Human Brain

Cited by 23 publications

References 24 publications

Investigating the Fractal Dimension of the Foveal Microvasculature in Relation to the Morphology of the Foveal Avascular Zone and to the Macular Circulation in Patients With Type 2 Diabetes Mellitus

Investigating the Fractal Dimension of the Foveal Microvasculature in Relation to the Morphology of the Foveal Avascular Zone and to the Macular Circulation in Patients With Type 2 Diabetes Mellitus

Two Dimensional Grayscale Images of the Aspiny Neurons from the Human Neostriatum: Monofractal and Gray Level Co-occurrence Matrix Analysis

Morphology and Fractal-Based Classifications of Neurons and Microglia in Two and Three Dimensions

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