Differential box counting methods for estimating fractal dimension of gray-scale images: A survey

Panigrahy, Chinmaya; Seal, Ayan; Mahato, Nihar Kumar; Bhattacharjee, Debotosh

doi:10.1016/j.chaos.2019.06.007

Cited by 81 publications

(23 citation statements)

References 48 publications

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“…Our study had a number of limitations. The algorithm to calculate the FD, despite its positive evaluation, still has some drawbacks that need to be addressed 45 . The scanned retinal area was 6 × 6 mm 2 ; however, our analysis had to be restricted to a square measuring only 2.555 × 2.555 mm 2 due to practical constraints whose future removal might allow for improvements.…”

Section: Discussionmentioning

confidence: 99%

Roughness of retinal layers in Alzheimer’s disease

Jáñez-García

Bachtoula

Salobrar‐García

et al. 2021

Sci Rep

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There is growing evidence that thinned retinal regions are interspersed with thickened regions in all retinal layers of patients with Alzheimer’s disease (AD), causing roughness to appear on layer thickness maps. The hypothesis is that roughness of retinal layers, assessed by the fractal dimension (FD) of their thickness maps, is an early biomarker of AD. Ten retinal layers have been studied in macular volumes of optical coherence tomography from 24 healthy volunteers and 19 patients with mild AD (Mini-Mental State Examination 23.42 ± 3.11). Results show that FD of retinal layers is greater in the AD group, the differences being statistically significant (p < 0.05). Correlation of layer FD with cognitive score, visual acuity and age reach statistical significance at 7 layers. Nearly all (44 out of 45) FD correlations among layers are positive and half of them reached statistical significance (p < 0.05). Factor analysis unveiled two independent factors identified as the dysregulation of the choroidal vascular network and the retinal inflammatory process. Conclusions: surface roughness is a holistic feature of retinal layers that can be assessed by the FD of their thickness maps and it is an early biomarker of AD.

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Section: Discussionmentioning

confidence: 99%

Roughness of retinal layers in Alzheimer’s disease

Jáñez-García

Bachtoula

Salobrar‐García

et al. 2021

Sci Rep

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“…It should be mentioned that color images are generally converted to grayscale and their FDs or their roughness are then calculated. (Panigrahy, Seal et al 2019)…”

Section: Methodsmentioning

confidence: 99%

Fractal dimension of EEG signal senses complexity of fractal animations

Khosrowabadi

2021

Preprint

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We are surrounded with many fractal and self-similar patterns which has been area of many researches in the recent years. We can perceive self-similarities in various spatial and temporal scales; however, the underlying neural mechanism needs to be well understood. In this study, we hypothesized that complexity of visual stimuli directly influence complexity of information processing in the brain. Therefore, changes in fractal pattern of EEG signal must follow change in fractal dimension of animation. To investigate this hypothesis, we recorded EEG signal of fifteen healthy participants while they were exposed to several 2D fractal animations. Fractal dimension of each frame of the animation was estimated by box counting method. Subsequently, fractal dimensions of 32 EEG channels were estimated in a frequency specific manner. Then, association between pattern of fractal dimensions of the animations and pattern of fractal dimensions of EEG signals were calculated using the Pearson's correlation algorithm. The results indicated that fractal animation complexity is mainly sensed by changes in fractal dimension of EEG signals at the centro-parietal and parietal regions. It may indicate that when the complexity of visual stimuli increases the mechanism of information processing in the brain also enhances its complexity to better attend and comprehend the stimuli.

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“…2: Three common steps in hyperspectral texture analysis. matrix or GLCM [22]), thresholding of neighborhood (local binary pattern or LBP [23]), spatial frequency analysis (Gabor filter [24], wavelets [25]), and model-based methods (autoregressive [26], Markovian [27], fractals [28], [29]). On the other hand, the works on procedural and dynamic textures [30], [31] are combining model-based and transform-based approaches for texture fidelity or segmentation purposes considering the correlation with human visual perception.…”

Section: Texture Feature Extractionmentioning

confidence: 99%

Hyperspectral Texture Metrology Based on Joint Probability of Spectral and Spatial Distribution

Chu¹,

Richard²,

Chatoux³

et al. 2021

IEEE Trans. on Image Process.

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Texture characterization from the metrological point of view is addressed in order to establish a physically relevant and directly interpretable feature. In this regard, a generic formulation is proposed to simultaneously capture the spectral and spatial complexity in hyperspectral images. The feature, named relative spectral difference occurrence matrix (RSDOM) is thus constructed in a multireference, multidirectional, and multiscale context. As validation, its performance is assessed in three versatile tasks. In texture classification on HyTexiLa, content-based image retrieval (CBIR) on ICONES-HSI, and land cover classification on Salinas, RSDOM registers 98.5% accuracy, 80.3% precision (for the top 10 retrieved images), and 96.0% accuracy (after post-processing) respectively, outcompeting GLCM, Gabor filter, LBP, SVM, CCF, CNN, and GCN. Analysis shows the advantage of RSDOM in terms of feature size (a mere 126, 30, and 20 scalars using GMM in order of the three tasks) as well as metrological validity in texture representation regardless of the spectral range, resolution, and number of bands.

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Differential box counting methods for estimating fractal dimension of gray-scale images: A survey

Cited by 81 publications

References 48 publications

Roughness of retinal layers in Alzheimer’s disease

Roughness of retinal layers in Alzheimer’s disease

Fractal dimension of EEG signal senses complexity of fractal animations

Hyperspectral Texture Metrology Based on Joint Probability of Spectral and Spatial Distribution

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