Fast simulation of ultrasound images from a CT volume

Dillenseger, Jean-Louis; Laguitton, Soizic; Delabrousse, íric

doi:10.1016/j.compbiomed.2008.12.009

Cited by 32 publications

(21 citation statements)

References 12 publications

(16 reference statements)

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“…Firstly, we evaluate the performance of GMM, BGMM, RMM, and BRMM for synthetic US images that simulated from CT images [10]. For this experiment, we set Kj=2, and L=2.…”

Section: Methodsmentioning

confidence: 99%

Bounded Rayleigh mixture model for ultrasound image segmentation

Bi¹,

Tang²,

Hui³

et al. 2017

SPIE Proceedings

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The finite mixture model based on the Gaussian distribution is a flexible and powerful tool to address image segmentation. However, in the case of ultrasound images, the intensity distributions are non-symmetric whereas the Gaussian distribution is symmetric. In this study, a new finite bounded Rayleigh distribution is proposed. One advantage of the proposed model is that Rayleigh distribution is non-symmetric which has ability to fit the shape of medical ultrasound data. Another advantage is that each component of the proposed model is suitable for the ultrasound image segmentation. We also apply the bounded Rayleigh mixture model in order to improve the accuracy and to reduce the computational time. Experiments show that the proposed model outperforms the state-of-art methods on time consumption and accuracy.

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“…Firstly, we evaluate the performance of GMM, BGMM, RMM, and BRMM for synthetic US images that simulated from CT images [10]. For this experiment, we set Kj=2, and L=2.…”

Section: Methodsmentioning

confidence: 99%

Bounded Rayleigh mixture model for ultrasound image segmentation

Bi¹,

Tang²,

Hui³

et al. 2017

SPIE Proceedings

View full text Add to dashboard Cite

show abstract

“…Each tissue (region) of these images was characterized by a specific acoustic impedance and a spatial distribution of scatterers. The simulated method predicted the appearance and properties of a BScan US image from a probe model and these region parameters [31]. The parameters dened in [31] were used for the simulated Abdomen Image.…”

Section: Experiments On Synthetic Datamentioning

confidence: 99%

“…The simulated method predicted the appearance and properties of a BScan US image from a probe model and these region parameters [31]. The parameters dened in [31] were used for the simulated Abdomen Image. For the Elliptical Image, we assigned to the elliptic and the background region the parameters dened in [31] for liver and fat, respectively.…”

Section: Experiments On Synthetic Datamentioning

confidence: 99%

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Fast segmentation of ultrasound images by incorporating spatial information into Rayleigh mixture model

Tang

Yang

et al. 2017

IET image process

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As a particular case of the Finite Mixture Model (FMM), Rayleigh Mixture Model (RMM) is considered as a useful tool for medical ultrasound (US) image segmentation. However, conventional RMM relies on intensity distribution only and does not take any spatial information into account that leads to misclassification on boundaries and inhomogeneous regions. In this paper, we propose an improved RMM with Neighbour information (RMMN) to solve this problem by introducing neighbourhood information through a mean template. The incorporation of the spatial information made RMMN more robust to noise on the boundaries. The size of the window which incorporates neighbour information was resized adaptively according to the local gradient distribution. We evaluated our model on experiments on synthetic data and real US images used by High-Intensity Focused Ultrasound (HIFU) therapy. On this data, we demonstrated that that the proposed model outperforms several state-of-art methods in terms of both segmentation accuracy and computation time.

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“…In the study of Dillenseger et al, 9 the scatterers were distributed using the 1D marked regularity model and a fast Hilbert filling curves algorithm. Their CT images were segmented into air, fat, soft tissue and bone.…”

Section: Simulation Of the Fishmentioning

confidence: 99%

Simulation of ultrasound backscatter images from fish

et al. 2011

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The objective of this work is to investigate ultrasound (US) backscatter in the MHz range from fish to develop a realistic and reliable simulation model. The long term objective of the work is to develop the needed signal processing for fish species differentiation using US. In in-vitro experiments, a cod (Gadus morhua) was scanned with both a BK Medical ProFocus 2202 ultrasound scanner and a Toshiba Aquilion ONE computed tomography (CT) scanner. The US images of the fish were compared with US images created using the ultrasound simulation program Field II. The center frequency of the transducer is 10 MHz and the Full Width at Half Maximum (FWHM) at the focus point is 0.54 mm in the lateral direction. The transducer model in Field II was calibrated using a wire phantom to validate the simulated point spread function. The inputs to the simulation were the CT image data of the fish converted to simulated scatter maps. The positions of the point scatterers were assumed to be uniformly distributed. The scatter amplitudes were generated with a new method based on the segmented CT data in Hounsfield Units and backscatter data for the different types of tissues from the literature. The simulated US images reproduce most of the important characteristics of the measured US image.

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Fast simulation of ultrasound images from a CT volume

Cited by 32 publications

References 12 publications

Bounded Rayleigh mixture model for ultrasound image segmentation

Bounded Rayleigh mixture model for ultrasound image segmentation

Fast segmentation of ultrasound images by incorporating spatial information into Rayleigh mixture model

Simulation of ultrasound backscatter images from fish

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