Automatic localization of curvilinear object in 3D ultrasound images

Barva, Martin; Kybic, Jan; Mari, Jean-Martial; Cachard, Christian; Hlaváč, Václav

doi:10.1117/12.594763

Cited by 14 publications

(24 citation statements)

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“…The study presented herein is based on a model-fitting approach described by Barva [19]. Given a 3-D ultrasound image (see Fig.…”

Section: Methodsmentioning

confidence: 99%

“…The cost function is smooth (unlike q AxShp (x; H), used in [19]), which is important for the local optimization later.…”

Section: ) Axshp Modelmentioning

confidence: 99%

“…Otherwise, the threshold is chosen empirically. Barva estimates the threshold as the 95% quantile of the input data by fitting a Gamma distribution [19], which seems to work in practice. This is based on the expectation that the proportion of voxels belonging to the tool is less than 5%.…”

Section: A Thresholdingmentioning

confidence: 99%

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Model Fitting Using RANSAC for Surgical Tool Localization in 3-D Ultrasound Images

Uhercik

Kybic

Liebgott

et al. 2010

IEEE Trans. Biomed. Eng.

124

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Abstract-Ultrasound guidance is used for many surgical interventions such as biopsy and electrode insertion. We present a method to localize a thin surgical tool such as a biopsy needle or a microelectrode in a 3-D ultrasound image. The proposed method starts with thresholding and model fitting using random sample consensus for robust localization of the axis. Subsequent local optimization refines its position. Two different tool image models are presented: one is simple and fast and the second uses learned a priori information about the tool's voxel intensities and the background. Finally, the tip of the tool is localized by finding an intensity drop along the axis. The simulation study shows that our algorithm can localize the tool at nearly real-time speed, even using a MATLAB implementation, with accuracy better than 1 mm. In an experimental comparison with several alternative localization methods, our method appears to be the fastest and the most robust one. We also show the results on real 3-D ultrasound data from a PVA cryogel phantom, turkey breast, and breast biopsy.

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“…The study presented herein is based on a model-fitting approach described by Barva [19]. Given a 3-D ultrasound image (see Fig.…”

Section: Methodsmentioning

confidence: 99%

“…The cost function is smooth (unlike q AxShp (x; H), used in [19]), which is important for the local optimization later.…”

Section: ) Axshp Modelmentioning

confidence: 99%

See 1 more Smart Citation

Model Fitting Using RANSAC for Surgical Tool Localization in 3-D Ultrasound Images

Uhercik

Kybic

Liebgott

et al. 2010

IEEE Trans. Biomed. Eng.

124

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show abstract

“…Most tool localization methods in thresholded 3D ultrasound images are based on Hough Transform(HT) for straight needle [7], and on the model-fitting approach [17], [16] for curved tool. Random sample consensus (RANSAC) algorithm [18] is used to compute the parameters of the model Fig.…”

Section: Needle Localizationmentioning

confidence: 99%

Automatic needle localization in 3D ultrasound images for brachytherapy

Younes

Voros

Troccaz

2018

2018 IEEE 15th International Symposium on Biomedical Imaging (ISBI 2018)

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Needle segmentation in ultrasound images may be the indispensable step to solve other problems, such as the detection of radioactive seeds in ultrasound images for brachytherapy treatment. In this paper we propose a novel method to localize accurately curved paths of flexible needles in threedimensional (3D) ultrasound images. Our method is based on an automatic thresholding step where the bayesian classifier theory is applied to select a needle's voxels from the background. The next step consists in detecting the needle and representing its curvature. Here we propose to fit a parametric polynomial of low degree to representing the parabolic curvature. We describe a first evaluation on a data set of 9 patients.

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“…1 depicts an example of a 3D ultrasound image of a polyvinyl alcohol (PVA) cryogel phantom [4] containing an electrode in water. In publications [5], [6] we presented two algorithms that permit to automatically determine the electrode position from 3D ultrasound images. First method is based on maximizing the parallel projection of input 3D image.…”

Section: Introductionmentioning

confidence: 99%

P3A-3 Comparison of Methods for Tool Localization in Biological Tissue from 3D Ultrasound Data

Barva¹,

Kybic²,

Hlaváč³

et al. 2006

2006 IEEE Ultrasonics Symposium

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Abstract-In medical applications, miniature surgical instruments such as needles, or electrodes are introduced into human body. The position of instrument in tissue can be estimated using 3D ultrasound. In previous publications, we introduced two novel algorithms for automatic electrode localization from 3D ultrasound images. The first method is based on the Parallel Integral Projection (PIP) transform, a modification of the Radon transform. We showed that the axis of the electrode can be estimated from the maximum of PIP transformation. To accelerate search for the maximum, a hierarchical mesh-grid algorithm is implemented. In second method, the electrode axis is described by a cubic polynomial. The distribution of voxel intensities inside the electrode region is a priori estimated from acquired data. The model parameters are robustly estimated using the RANSAC estimator. In this paper, their performance in terms of accuracy is compared. A series of tests on numerical phantoms created with the FIELD II simulation program were performed to quantitatively evaluate the localization accuracy. We observed a decrease in accuracy when artificial noise was added to the input data. The algorithms were also tested on real ultrasound data of a cryogel phantom comprising metallic electrode.

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Automatic localization of curvilinear object in 3D ultrasound images

Cited by 14 publications

References 0 publications

Model Fitting Using RANSAC for Surgical Tool Localization in 3-D Ultrasound Images

Model Fitting Using RANSAC for Surgical Tool Localization in 3-D Ultrasound Images

Automatic needle localization in 3D ultrasound images for brachytherapy

P3A-3 Comparison of Methods for Tool Localization in Biological Tissue from 3D Ultrasound Data

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