Fractal Analysis of Trabecular Bone Texture on Radiographs: Discriminant Value in Postmenopausal Osteoporosis

Pothuaud, Laurent; Lespessailles, Éric; Harba, Rachid; Jennane, Rachid; Royant, Valérie; Eynard, E.; Cl, Benhamou

doi:10.1007/s001980050108

Cited by 146 publications

(88 citation statements)

References 28 publications

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“…The fBm, and its characterization by estimation of its Hurst parameter value, is largely used in texture analysis, e.g., [8,14,10,11,13,4]. However, the fBm is an homogeneous process (the process is statistically the same on the whole space), and its high and low frequency contents are equivalent (the process is the same whatever the scale of observance).…”

Section: The Imsfbm Texture Modelmentioning

confidence: 99%

Classification of Texture Images using Multi-scale StatisticalEstimators of Fractal Parameters

Deguy¹,

Debain²,

Benassi³

2000

Procedings of the British Machine Vision Conference 2000

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We present a new method of fractal-based texture analysis, using the multiscale fractional Brownian motion texture model, and a new parameter, intermittency. The intermittency parameter Ô describes a degree of presence of the textural information: a low value of Ô implies a very lacunar texture. The multi-scale fractional Brownian motion model allows to construct multiregime textures in the frequency domain. Adding intermittency to this model, we compose the intermittent multi-scale fractional Brownian motion model: the Hurst and intermittency parameters of such processes are functions À´Ðµ and Ô´Ðµ depending on a scale Ð. The texture is thereby seen as the fusion of structures and details. The structure of the texture is analyzed with the large values of Ð, corresponding to the low frequency content of the texture. The details of the texture are analyzed with the small values of Ð, related to the high frequency content of the texture. The texture is then characterized by all the estimated values of À´Ðµ and Ô´Ðµ, for all the scales Ð of analysis. The method allows a multi-frequency analysis, permitting the choice of significant scales in a classification task. An application to the classification of corn silage texture images, for which the low frequency content is determining, is proposed.

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Section: The Imsfbm Texture Modelmentioning

confidence: 99%

Classification of Texture Images using Multi-scale StatisticalEstimators of Fractal Parameters

Deguy¹,

Debain²,

Benassi³

2000

Procedings of the British Machine Vision Conference 2000

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“…Clinical results have also pointed out the strong potential of the H parameter of fBm in quantifying osteoporosis on bone X-ray radiographs (Pothuaud et al, 1998;Lespessailles et al, 1998). …”

Section: Introductionmentioning

confidence: 99%

Estimation of the 3D self-similarity parameter of trabecular bone from its 2D projection

Jennane

Harba

Lemineur

et al. 2007

Medical Image Analysis

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It has been shown that the analysis of two dimensional (2D) bone X-ray images based on the fractional Brownian motion (fBm) model is a good indicator for quantifying alterations in the three dimensional (3D) bone micro-architecture. However, this 2D measurement is not a direct assessment of the 3D bone properties. In this paper, we first show that S 3D , the self-similarity parameter of 3D fBm, is linked to S 2D , that of its 2D projection, by S 3D = S 2D -0.5. In the light of this theoretical result, we have experimentally examined whether this relation holds for trabecular bone. Twenty one specimens of trabecular bone were derived from frozen human femoral heads. They were digitized using a high resolution -CT. Their projections were simulated numerically by summing the data in the three orthogonal directions and both 3D and 2D self-similarity parameters were measured. Results show that the self-similarity of the 3D bone volumes and that of their projections are linked by the previous equation. This demonstrates that a simple projection provides 3D information about the bone structure. This information can be a valuable adjunct to the bone mineral density for the early diagnosis of osteoporosis.

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“…Actually, among many methods used to extract information from images of any kind, and in particular medical images, fractal methods are efficient candidates. [30][31][32][33][34] The flexibility, generality, and robustness of these methods make them adaptable to the treatment of real and complex signals. Of the fractal methods, fractional Brownian motion is commonly used.…”

Section: Introductionmentioning

confidence: 99%

Noninvasive radiation burn diagnosis using speckle phenomenon with a fractal approach to processing

2010

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Abstract. Radiation burns account for the vast majority of damage by accidental radiation exposure. They are characterized by successive and unpredictable inflammatory bursts that are preceded by a clinically latent postirradiation period. Diagnosis and prognosis of the clinical course of radiation burns have proven to be a difficult task. In a classical clinical setting, no technique can distinguish irradiated versus healthy skin during the clinically latent period, hence development of new tools is required. This work describes a noninvasive technique based on speckle phenomenon, designed to support radiation burn diagnosis and prognosis. Speckle produced by strongly scattering media contains information about their optical properties. The difficulty is to extract significant information from speckle patterns to discriminate between strongly scattering media and to characterize any change. Speckle patterns from irradiated and nonirradiated porcine skins are recorded in vivo several times after radiation exposure. A fractal approach is used in the treatment of speckle patterns. The results show that this technique allows discrimination between healthy and irradiated skin, in particular during the clinically latent period ͑p Ͻ 0.01͒. Parameters extracted from speckle patterns discriminate and vary differently with radiation, which means they represent different information about skin changes.

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Fractal Analysis of Trabecular Bone Texture on Radiographs: Discriminant Value in Postmenopausal Osteoporosis

Cited by 146 publications

References 28 publications

Classification of Texture Images using Multi-scale StatisticalEstimators of Fractal Parameters

Classification of Texture Images using Multi-scale StatisticalEstimators of Fractal Parameters

Estimation of the 3D self-similarity parameter of trabecular bone from its 2D projection

Noninvasive radiation burn diagnosis using speckle phenomenon with a fractal approach to processing

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