&lt;title&gt;Scale-space and boundary detection in ultrasonic imaging using nonlinear signal-adaptive anisotropic diffusion&lt;/title&gt;

Steen, Erik; Olstad, Bjoern

doi:10.1117/12.175047

Cited by 7 publications

(3 citation statements)

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“…• Gray level distribution: As noted in the previous section, the Rayleigh model of speckle has proved a popular choice. A Rayleigh distribution was used in the anisotropic diffusion edge detection method of [211] and in statistical segmentation methods in [11], [43], [136], [137], [144], [151]. Most recently the Rayleigh distribution was incorporated into the level set method of Sarti et al [38] and of Cardinal et al [170].…”

Section: Priors 1) Image Featuresmentioning

confidence: 99%

Ultrasound image segmentation: a survey

Noble

Boukerroui

2006

IEEE Trans. Med. Imaging

956

489

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This paper reviews ultrasound segmentation paper methods, in a broad sense, focusing on techniques developed for medical B-mode ultrasound images. First, we present a review of articles by clinical application to highlight the approaches that have been investigated and degree of validation that has been done in different clinical domains. Then, we present a classification of methodology in terms of use of prior information. We conclude by selecting ten papers which have presented original ideas that have demonstrated particular clinical usefulness or potential specific to the ultrasound segmentation problem.

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Section: Priors 1) Image Featuresmentioning

confidence: 99%

Ultrasound image segmentation: a survey

Noble

Boukerroui

2006

IEEE Trans. Med. Imaging

956

489

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“…Contour estimation (from a frame or from a sequence of frames) has been the main objective o f most works on semi-automatic or automatic feature extraction from echocardiographic data 10 17] this is a consequence of the huge amount of structural information carried by boundaries. Moreover, contours are the base of most quantitative procedures.…”

Section: A Previous Work On Contour Estimationmentioning

confidence: 99%

“…Suppose that frames I(n;1) and I(n) are observed. Assuming the observation model presented in Section III,and applying the Bayes law, the joint probability o f r(n), r(n ; 1), I(n ; 1) and I(n) can beexpanded as P(r(n) I(n) r(n ; 1) I(n ; 1)) = P(I(n)jr(n))P(r(n) r(n ; 1) I(n ; 1)) = P(I(n)jr(n))P(I(n ; 1)jr(n ; 1))P (r(n ; 1) r(n)) = P(I(n)jr(n))P(I(n ; 1)jr(n ; 1))P (r(n ; 1)jr(n))P (r(n)): (17) r(n) = r(n ; 1) + "(n) (18) with "(n) being a zero mean white Gaussian noise vector whose elements have variances…”

Section: A Modeling Image Sequencesmentioning

confidence: 99%

Wall position and thickness estimation from sequences of echocardiographic images

Dias

Leitao

1996

IEEE Trans. Med. Imaging

130

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This paper presents a new method for endocardial (inner) and epicardial (outer) contour estimation from sequences of echocardiographic images. The framework herein introduced is ne-tuned for parasternal short axis views at the papillary muscle level. The underlying model is probabilistic it captures the relevant features of the image generation physical mechanisms and of the heart morphology. Contour sequences are assumed two-dimensional noncausal rst order Markov random processes each variable has a spatial index and a temporal index. The image pixels are modelled as Rayleigh distributed random variables with means depending on their positions (inside endocardium, between endocardium and pericardium, or outside pericardium). The complete probabilistic model is built under the Bayesian framework. As estimation criterion the maximum a posteriori (MAP) is adopted. To solve the optimization problem one is led to (joint estimation of contours and distributions' parameters), we introduce an algorithm herein named iterative multigrid dynamic programming (IMDP). It is a fully data driven scheme with no ad-hoc parameters. The method is implemented on an ordinary workstation, leading to computation times compatible with operational use. Experiments with simulated and real images are presented.Key words: Echocardiography, Contour Estimation, Bayesian, Dynamic Programming. 1Refererence 1] is a short version of this work.

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Medical Image Processing

Noble

1999

Wiley Encyclopedia of Electrical and Electronics Engineering

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<title>Scale-space and boundary detection in ultrasonic imaging using nonlinear signal-adaptive anisotropic diffusion</title>

Cited by 7 publications

References 0 publications

Ultrasound image segmentation: a survey

Ultrasound image segmentation: a survey

Wall position and thickness estimation from sequences of echocardiographic images

Medical Image Processing

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