Local fractal and multifractal features for volumic texture characterization

Lopes, Renaud; Dubois, Patrick; Bhouri, Imen; Bedoui, Mohamed Hédi; Maouche, S.; Betrouni, Nacim

doi:10.1016/j.patcog.2011.02.017

Cited by 54 publications

(32 citation statements)

References 37 publications

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“…Fractal dimension from volumetric data is used to characterize 3-D textures by Alberich-Bayarri et al (2010) and Ollivier et al (2013) by computing the three-dimensional version of the Minkowski-Bouligand dimension (also called box-counting dimension, Lopes et al, 2011b). It consists of counting the number of boxes of decreasing sizes required to encompass the contour of binarized textures.…”

Section: Fractalsmentioning

confidence: 99%

Three-dimensional solid texture analysis in biomedical imaging: Review and opportunities

Depeursinge

Foncubierta–Rodríguez

Ville

et al. 2014

Medical Image Analysis

201

146

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a b s t r a c tThree-dimensional computerized characterization of biomedical solid textures is key to large-scale and high-throughput screening of imaging data. Such data increasingly become available in the clinical and research environments with an ever increasing spatial resolution.In this text we exhaustively analyze the state-of-the-art in 3-D biomedical texture analysis to identify the specific needs of the application domains and extract promising trends in image processing algorithms. The geometrical properties of biomedical textures are studied both in their natural space and on digitized lattices. It is found that most of the tissue types have strong multi-scale directional properties, that are well captured by imaging protocols with high resolutions and spherical spatial transfer functions. The information modeled by the various image processing techniques is analyzed and visualized by displaying their 3-D texture primitives. We demonstrate that non-convolutional approaches are expected to provide best results when the size of structures are inferior to five voxels. For larger structures, it is shown that only multi-scale directional convolutional approaches that are non-separable allow for an unbiased modeling of 3-D biomedical textures. With the increase of high-resolution isotropic imaging protocols in clinical routine and research, these models are expected to best leverage the wealth of 3-D biomedical texture analysis in the future. Future research directions and opportunities are proposed to efficiently model personalized image-based phenotypes of normal biomedical tissue and its alterations. The integration of the clinical and genomic context is expected to better explain the intra class variation of healthy biomedical textures. Using texture synthesis, this provides the exciting opportunity to simulate and visualize texture atlases of normal ageing process and disease progression for enhanced treatment planning and clinical care management.

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Section: Fractalsmentioning

confidence: 99%

Three-dimensional solid texture analysis in biomedical imaging: Review and opportunities

Depeursinge

Foncubierta–Rodríguez

Ville

et al. 2014

Medical Image Analysis

201

146

View full text Add to dashboard Cite

show abstract

“…One is local singularity exponent, namely Hölder coefficients [3,4], based on capacity measure, denoted as ˛c, the other is multifractal dimensions D q based on differential box-counting (MDBC). The local singularity coefficients ˛c features, defined as ˛c ≡ lim ε→0 ln (D r )/ ln(ε), can well describe the characteristic of a texture image and be used in lots of fields [13,23,27]. In Ref.…”

Section: Segmentation Experimentsmentioning

confidence: 99%

“…Hence, it can bring us a more efficacious way to process various texture recognition problems by the MFA [2][3][4]22,23,27]. Such as, Xu et al [22] proposed a robust texture descriptor combining the MFA and Gabor filter.…”

Section: Introductionmentioning

confidence: 99%

Local multifractal detrended fluctuation analysis for non-stationary image's texture segmentation

Wang

Zong-shou

2014

Applied Surface Science

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“…As a matter of fact, fractals are more popular in analysis of remotely sensed images because these images are rich in irregular objects like water bodies, land covers, erratic costal lines and so on which are to be analysed with the help of fractals [3]. Texture analysis is another field where fractals became popular and the reason behind this fact is the similarity in estimation of texture and fractal features [3,[7][8][9]. Since texture is a property of neighbourhood which is measured for a local window of pixels, the same procedure is applied to estimate fractal features too.…”

Section: Introductionmentioning

confidence: 99%

“…Thus, estimation of fractal features requires a keen attention, so that accurate methods could be developed accordingly. Fractal dimension, the most popular fractal feature, can be estimated for digital images by considering the pixel values of predefined neighbourhoods as done in numerous available methods [1,3,[9][10][11][12][13][14][15][16], viz. differential box counting method [17], Triangular prism surface area method (TPSAM) [18,19], Fourier spectrum based method [2,3], Variogram method, Robust fractal dimension estimation [3], 2D variation method [20] which deal with image pixels and their orientation at a varying scale.…”

Section: Introductionmentioning

confidence: 99%