Retinal Vascular Fractal Dimensions and Their Association with Macrovascular Cardiac Disease

Dinesen, Sebastian; Jensen, Peter Bjødstrup; Bloksgaard, Maria; Blindbæk, Søren Leer; Mey, Jo G. R. De; Rasmussen, Lars Melholt; Lindholt, Jes Sanddal; Grauslund, Jakob

doi:10.1159/000514442

Cited by 7 publications

(7 citation statements)

References 26 publications

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“…FD can provide information relevant to the complex geometrical structure of the vascular tree and it can be defined as a measure of irregularly shaped objects and fragmental property [23,24]. In the literature, several fractal implementation have been developed.…”

Section: Fractal Dimensionmentioning

confidence: 99%

Assessing vascular complexity of PAOD patients by deep learning-based segmentation and fractal dimension

Bruno

Spadea

Scaramuzzino

et al. 2022

Neural Comput & Applic

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The assessment of vascular complexity in the lower limbs provides relevant information about peripheral artery occlusive diseases (PAOD), thus fostering improvements both in therapeutic decisions and prognostic estimation. The current clinical practice consists of visually inspecting and evaluating cine-angiograms of the interested region, which is largely operator-dependent. We present here an automatic method for segmenting the vessel tree and compute a quantitative measure, in terms of fractal dimension (FD), of the vascular complexity. The proposed workflow consists of three main steps: (i) conversion of the cine-angiographies to single static images with a broader field of view, (ii) automatic segmentation of the vascular trees, and (iii) calculation and assessment of FD as complexity index. In particular, this work defines (1) a method to reduce the inter-observer variability in judging vascular complexity in cine-angiography images from patients affected by peripheral artery occlusive disease (PAOD), and (2) the use of Fractal Dimension as a metric of shape complexity of vascular tree. The inter-class correlation coefficient (ICC) is computed as inter-observer agreement metric and to account for possible systematic error, that depends on the experience of the raters. The automatic segmentation of vascular tree achieved an Area Under the Curve mean value of $$0.77~\pm ~0.07$$ 0.77 ± 0.07 , with a min-max range of $$0.57-0.87$$ 0.57 - 0.87 . Absolute operator agreement was higher over the segmented image ($$ICC=0.96$$ I C C = 0.96 ) compared to the video ($$ICC=0.76$$ I C C = 0.76 ) and the a broader field of view image ($$ICC=0.92$$ I C C = 0.92 ). Fractal Dimension computed on both manual segmented images (ground truths) and automatically showed a good correlation with the clinical score (0.85 and 0.75, respectively). Experimental analyses suggest that extracting the vascular tree from cine-angiography can substantially improve the reliability of visual assessment of vascular complexity in PAOD. Results also reveal the effectiveness of FD in evaluating complex vascular tree structures.

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Section: Fractal Dimensionmentioning

confidence: 99%

Assessing vascular complexity of PAOD patients by deep learning-based segmentation and fractal dimension

Bruno

Spadea

Scaramuzzino

et al. 2022

Neural Comput & Applic

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“…The use of bulbar conjunctival vessel length density, i.e., the length of bulbar conjunctival vessels per unit area [ 6 ], can exclude the effect of vessel diameter. Another way is fractal, an algorithm that reflects structural complexity and irregularities, and fractal dimensions can reflect the complexity and density of blood vessels [ 7 ]. Liu et al [ 8 ] confirmed that the fractal dimension of the bulbar conjunctival vessels is positively correlated with the bulbar conjunctival vessel density obtained by pixel counting.…”

Section: Bulbar Conjunctival Microcirculation Parametersmentioning

confidence: 99%

Progress of Bulbar Conjunctival Microcirculation Alterations in the Diagnosis of Ocular Diseases

Sun

Liu

et al. 2022

Disease Markers

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Bulbar conjunctival microcirculation is a microvascular system distributed in the translucent bulbar conjunctiva near the corneal limbus. Multiple ocular diseases lead to bulbar conjunctival microcirculation alterations, which means that bulbar conjunctival microcirculation alterations would be potential screening and diagnostic indicators for these ocular diseases. In recent years, with the emergence and application of a variety of noninvasive observation devices for bulbar conjunctiva microcirculation and new image processing technologies, studies that explored the potential of bulbar conjunctival microcirculation alterations in the diagnosis of ocular diseases have been emerging. However, the potential of bulbar conjunctival microcirculation alterations as indicators for ocular diseases has not been exploited to full advantage. The observation devices, image processing methods, and algorithms are not unified. And large-scale research is needed to concrete bulbar conjunctival microcirculation alterations as indicators for ocular diseases. In this paper, we provide an update on the progress of bulbar conjunctival microcirculation alterations in the diagnosis of ocular diseases in recent five years (from January 2017 to March 2022). Relevant ocular diseases include contact lens wearing, dry eye, conjunctival malignant melanoma, conjunctival nevus, and diabetic retinopathy.

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“…Where denotes the grid cell size (a place-designated neuron) and the number of cells. The technique for determining the box-totaling dimension is computed similarly in information theory (entropy theory) and by employing values of the item, as follows: [28,29]…”

Section: Multi-dimensional Fractal (Mdf)mentioning

confidence: 99%

“…A medicinal picture verification technique is accomplished in the researchers' attempt in [24] related to wavelet reestablishment and fractal dimension analysis. The images are eliminated, and a fractal property is formed as the verification image of the pictures when rendering to the singularity of the fractal dimension of the block data [25][26][27][28][29][30][31][32][33][34][35][36][37][38][39].…”

Section: Introductionmentioning

confidence: 99%

Images Enhancement Based on a New Multi-Dimensional Fractal Created by Rectangular Function

Farhan¹

2022

ETJ

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 New Multi-Dimensional Fractal was introduced to improve and enhance the images.  Different images with low gray levels were enhanced by applying the suggested enhancement model.  The improvement brought a 97% rate in PNSR and a 95% rate in RMSD.Digital image processing is a field that is included in many journals due to its importance and the fact that it facilitates the achievement of many scientific and engineering applications worldwide. This specialization is linked with other disciplines, whether medical, engineering, sports, and others, as it facilitates the completion of applications quickly and efficiently. Researchers have discovered and garnered notice as a promising analytic tool in image processing using the idea of fractal dimension. In this effort, a new Multi-Dimensional Fractal (MDF) in view of the rectangle function was introduced a. As an application, the MDF to improve and enhance the images was employed, and found that there is a connection between MDF and image processing, where the self-similarity property, for example, is one of several features in the new definition. Other properties are discussed in the sequel, including image noise reduction. The presence of noise is responsible for properly operating these images in various applications. Several academics have created and applied a strategy for minimizing noise in features multiplicatively throughout the last several years. The outcomes reveal that the proposed strategy is successful. The method is based on the definition of the rectangular function (the elementary component of all digital signals, videos, and images), where this function indicates a rectangular-formed rhythm that is concentrated at the origin. For example, the suggested process received a rate of 97% for PNSR and 95% for RMSD.

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Retinal Vascular Fractal Dimensions and Their Association with Macrovascular Cardiac Disease

Cited by 7 publications

References 26 publications

Assessing vascular complexity of PAOD patients by deep learning-based segmentation and fractal dimension

Assessing vascular complexity of PAOD patients by deep learning-based segmentation and fractal dimension

Progress of Bulbar Conjunctival Microcirculation Alterations in the Diagnosis of Ocular Diseases

Images Enhancement Based on a New Multi-Dimensional Fractal Created by Rectangular Function

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