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Journal ArticleDOI

Statistics of Speckle in Ultrasound B-Scans

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TLDR
In this article, the authors derived autocorrelation functions and power spectra derived from B-scans of a scattering phantom containing many scatterers per resolution cell, leading naturally to the definition of the average speckle spot or cell sue, and this inturn is comparable to the resolution cell.
Abstract
the of the magnitude, i.e., intensity, of the field.) It is shown that Rayleigh statistics govern the fist-order behavior of the magnitude; and the autocorrelation of the resulting image speckle is obtained by the methodof Middleton. The corresponding power spectrum follows immediately by Fourier transformation. Theoretical and experimentally determined autocorrelation functions and power spectra derived from B-scans of a scattering phantom containing many scatterers per resolution cell are presented. These functions lead naturally to the definition of the average speckle spot or cell sue, and this inturn is comparable to the resolution cell. Each independent speckle servesas a degreeof freedom that determines the number of samples of tissue available over a target.As the speckle cell size decreases this number increases in a manner predictable from the physical parameters of the cell size. However, it is found that the speckle cellis broadened, the degrees of freedom diminished, when the object structureis correlated. This yields the possibilityof deducing information about the object structure from the second-order statistics of the speckle texture, in addition to that obtainable from the fiistorder statistics.

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Journal ArticleDOI

Ultrasound image segmentation: a survey

TL;DR: This paper reviews ultrasound segmentation methods, in a broad sense, focusing on techniques developed for medical B-mode ultrasound images, and presents a classification of methodology in terms of use of prior information.
Journal ArticleDOI

Measurement of ventricular torsion by two-dimensional ultrasound speckle tracking imaging

TL;DR: The STI estimation of LVtor is concordant with those analyzed by tagged MRI and also showed good agreement with those by DTI (data derived from tissue velocity) and may make the assessment more available in clinical and research cardiology.
Journal ArticleDOI

An adaptive weighted median filter for speckle suppression in medical ultrasonic images

TL;DR: In this article, the adaptive weighted median filter (AWMF) is proposed for reducing speckle noise in medical ultrasonic images. But it is not suitable for image segmentation.
Journal ArticleDOI

A versatile wavelet domain noise filtration technique for medical imaging

TL;DR: A robust wavelet domain method for noise filtering in medical images that adapts itself to various types of image noise as well as to the preference of the medical expert; a single parameter can be used to balance the preservation of (expert-dependent) relevant details against the degree of noise reduction.
References
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Book

Probability, random variables and stochastic processes

TL;DR: This chapter discusses the concept of a Random Variable, the meaning of Probability, and the axioms of probability in terms of Markov Chains and Queueing Theory.
Journal ArticleDOI

Introduction to Fourier Optics

Joseph W. Goodman, +1 more
- 01 Apr 1969 - 
TL;DR: The second edition of this respected text considerably expands the original and reflects the tremendous advances made in the discipline since 1968 as discussed by the authors, with a special emphasis on applications to diffraction, imaging, optical data processing, and holography.
Book

Introduction to Fourier optics

TL;DR: The second edition of this respected text considerably expands the original and reflects the tremendous advances made in the discipline since 1968 as discussed by the authors, with a special emphasis on applications to diffraction, imaging, optical data processing, and holography.
Journal ArticleDOI

Mathematical analysis of random noise

TL;DR: In this paper, the authors used the representations of the noise currents given in Section 2.8 to derive some statistical properties of I(t) and its zeros and maxima.
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