Spatial speckle characterization by Brownian motion analysis

Guyot, Steve; Peron, Marie-Cécile; Deléchelle, E.

doi:10.1103/physreve.70.046618

Cited by 16 publications

(16 citation statements)

References 15 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…We can see that the speckle patterns present a decrease in 1 / f (where 0;1 ) for high frequencies. This behaviour is characteristics of a self-similar process [7]. …”

Section: Speckle Statistics Theorymentioning

confidence: 98%

“…Speckle from biological medium is time variant; therefore, from a signal processing point of view, the classical frequential approach seems to be not sufficient to study this non-stationary phenomenon. An original approach of the speckle was recently introduced in which a parallel with the fractal Brownian motion theory was proposed [7]. From this fractal approach, three stochastic parameters can be extracted from the speckle pattern: Hurst coefficient, the saturation of the variance and the self-similar element.…”

Section: Introductionmentioning

confidence: 99%

See 1 more Smart Citation

Speckle: tool for diagnosis assistance

et al. 2006

View full text Add to dashboard Cite

In this paper, we present a new approach of the speckle phenomenon. This method is based on the fractal Brownian motion theory and allows the extraction of three stochastic parameters to characterize the speckle pattern. For the first time, we present the results of this method applied to the discrimination of the healthy vs. pathologic skin. We also demonstrate, in case of the scleroderma, than this method is more accurate than the classical frequential approach.

show abstract

“…We can see that the speckle patterns present a decrease in 1 / f (where 0;1 ) for high frequencies. This behaviour is characteristics of a self-similar process [7]. …”

Section: Speckle Statistics Theorymentioning

confidence: 98%

Section: Introductionmentioning

confidence: 99%

Speckle: tool for diagnosis assistance

et al. 2006

View full text Add to dashboard Cite

show abstract

“…Guyot [23] studied the mathematical correlation between a fractional Brownian motion and speckle patterns and used successfully the diffusion equation to characterize skin psoriasis infections. In [24], Chen et al estimates the fractal dimension of an ultrasound image of breast lesion also using the fractal Brownian motion and integrates this estimation in a computer aided diagnosis to classify the lesions as benign or malign.…”

Section: Tissue Characterizationmentioning

confidence: 99%

Wavelet-based multifractal analysis of 1-D and 2-D signals: New results

Ouahabi

Femmam²

2011

Analog Integr Circ Sig Process

View full text Add to dashboard Cite

During the past decade, new tools stemming from fractal geometry and wavelet analysis are meeting with great success in signal image processing. This paper will focus on these two topics: Wavelets and Multifractal. Both themes evolved towards self contained theories, and yet, a host of reasons justify for coupling them in same applications. It is well known that both analyses share the same conceptual backbone of ''scale'': it is the ''mathematical zoom'' commonly associated to wavelet analysis and it is the ''scaling laws'' that underlie multifractal structures. Very naturally then, wavelets stood as a privileged tool for analyzing and characterizing multifractal signals and images. Hence, the purpose of this paper is to illuminate some of the issue involved in taking advantage of the current advances in wavelets and multifractals analysis. We discuss continuous, discrete, orthogonal wavelets and present applications to fracture processes and medical ultrasound imaging.

show abstract

“…35,54,56 From fractal theory, 57 a process X that presents such behavior according to the variable t is described for its autocorrelation function by:…”

Section: Speckle Pattern Processingmentioning

confidence: 99%

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

2010

View full text Add to dashboard Cite

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.

show abstract

Spatial speckle characterization by Brownian motion analysis

Cited by 16 publications

References 15 publications

Speckle: tool for diagnosis assistance

Speckle: tool for diagnosis assistance

Wavelet-based multifractal analysis of 1-D and 2-D signals: New results

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

Contact Info

Product

Resources

About