Maximum likelihood contour estimation using beta-statistics in ultrasound images

Martín, María del Carmen; Jose, Reshma; Alberola, C.

doi:10.1109/ispa.2001.938629

Cited by 2 publications

(3 citation statements)

References 5 publications

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“…The optimization approach was based on the relaxation labeling method [8] which provides suboptimal solutions. In [11] the problem of object detection in speckle images was addressed. A regularized maximum likelihood (ML) method was used to determine the contour of the object.…”

Section: State Of the Artmentioning

confidence: 99%

“…Using a Beta distribution [11] with parameters depending on the pixel positions with respect to the contour deformation we also define the log-likelihood function…”

Section: The Likelihood Functionmentioning

confidence: 99%

“…where β i (k, j) and β e (k, j) are partition subsets of β(k, j) with the internal and external set of pixels respect to the contour deformation point j at position k, respectively. The function L I (k, j) has 4J Beta shape parameters (α i 1 (j), α i 2 (j), α e 1 (j), α e 2 (j)) which can be estimated directly from the ultrasound image I [11].…”

Section: The Likelihood Functionmentioning

confidence: 99%

See 2 more Smart Citations

A Bayesian Approach to in vivo Kidney Ultrasound Contour Detection Using Markov Random Fields

Martín

Alberola

2002

Medical Image Computing and Computer-Assisted Intervention — MICCAI 2002

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Automatic detection of structures in medical images is of great importance for the implementation of tools that can obtain accurate measurements for an eventual diagnosis. In this paper, a new method for the creation of such tools is presented. We focus on in vivo kidney ultrasound, a target in which classical methods fail due to the inherent difficulty of such an imaging modality and organ. The proposed method operates on every slice by detecting kidney contours under a probabilistic Bayesian framework. We make use of Markov Random Fields ideas to model the problem and find the solution. A computer easy-to-use interface to the model is also presented.

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Section: State Of the Artmentioning

confidence: 99%

“…Using a Beta distribution [11] with parameters depending on the pixel positions with respect to the contour deformation we also define the log-likelihood function…”

Section: The Likelihood Functionmentioning

confidence: 99%

Section: The Likelihood Functionmentioning

confidence: 99%

See 1 more Smart Citation

A Bayesian Approach to in vivo Kidney Ultrasound Contour Detection Using Markov Random Fields

Martín

Alberola

2002

Medical Image Computing and Computer-Assisted Intervention — MICCAI 2002

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An Interacting Multiple Model Probabilistic Data Association Filter for Cavity Boundary Extraction From Ultrasound Images

Abolmaesumi

Sirouspour

2004

IEEE Trans. Med. Imaging

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This paper presents a novel segmentation technique for extracting cavity contours from ultrasound images. The problem is first discretized by projecting equispaced radii from an arbitrary seed point inside the cavity toward its boundary. The distance of the cavity boundary from the seed point is modeled by the trajectory of a moving object. The motion of this moving object is assumed to be governed by a finite set of dynamical models subject to uncertainty. Candidate edge points obtained along each radius include the measurement of the object position and some false returns. The modeling approach enables us to use the interacting multiple model estimator along with a probabilistic data association filter, for contour extraction. The convergence rate of the method is very fast because it does not employ any numerical optimization. The robustness and accuracy of the method are demonstrated by segmenting contours from a series of ultrasound images. The results are validated through comparison with manual segmentations performed by an expert. An application of the method in segmenting bone contours from computed tomography images is also presented.

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Maximum likelihood contour estimation using beta-statistics in ultrasound images

Cited by 2 publications

References 5 publications

A Bayesian Approach to in vivo Kidney Ultrasound Contour Detection Using Markov Random Fields

A Bayesian Approach to in vivo Kidney Ultrasound Contour Detection Using Markov Random Fields

An Interacting Multiple Model Probabilistic Data Association Filter for Cavity Boundary Extraction From Ultrasound Images

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