Compressed sensing of ultrasound images: Sampling of spatial and frequency domains

Quinsac, Céline; Basarab, Adrian; Girault, Jean‐Marc; Kouamé, Denis

doi:10.1109/sips.2010.5624793

Cited by 50 publications

(31 citation statements)

References 12 publications

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“…The method we have recently proposed for compressing ultrasound images consists of spatially sampling the image via different acquisition masks and then reconstructing the 2D Fourier transform (FT) of the decimated image [3,7]. Precisely, in presence of an additive measurement noise, the image can be reconstructed by the following optimization problem arg min…”

Section: Compressed Sensing In Ultrasonographymentioning

confidence: 99%

Bayesian compressed sensing in ultrasound imaging

Quinsac

Dobigeon

Basarab

et al. 2011

2011 4th IEEE International Workshop on Computational Advances in Multi-Sensor Adaptive Processing (CAMSAP)

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Following our previous study on compressed sensing for ultrasound imaging, this paper proposes to exploit the image sparsity in the frequency domain within a Bayesian approach. A Bernoulli-Gaussian prior is assigned to the Fourier transform of the ultrasound image in order to enforce sparsity and to reconstruct the image via Bayesian compressed sensing. In addition, the Bayesian approach allows the image sparsity level in the spectral domain to be estimated, a significant parameter in the ℓ1 constrained minimization problem related to compressed sensing. Results obtained with a simulated ultrasound image and an in vivo image of a human thyroid gland show a reconstruction performance similar to a classical compressed sensing algorithm from half of spatial samples while estimating the sparsity level during reconstruction.

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Section: Compressed Sensing In Ultrasonographymentioning

confidence: 99%

Bayesian compressed sensing in ultrasound imaging

Quinsac

Dobigeon

Basarab

et al. 2011

2011 4th IEEE International Workshop on Computational Advances in Multi-Sensor Adaptive Processing (CAMSAP)

Self Cite

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“…Since Kolmogorov-Smirnov test (KS test) is the most popular test for the similarity of time series we know of [27,21,17], we have decided to use KS test as the first similarity measure. Technically, KS test is a non-parametric statistical hypothesis testing method that can test whether two underlying one-dimensional probability distributions of random variables di↵er or not [16,9].…”

Section: Kolmogorov-smirnov Testmentioning

confidence: 99%

Novel Data Reduction Based on Statistical Similarity

Lee

Sim

Choi

et al. 2016

Proceedings of the 28th International Conference on Scientific and Statistical Database Management

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Applications such as scientific simulations and power grid monitoring are generating so much data quickly that compression is essential to reduce storage requirement or transmission capacity. To achieve better compression, one is often willing to discard some repeated information. These lossy compression methods are primarily designed to minimize the Euclidean distance between the original data and the compressed data. But this measure of distance severely limits either reconstruction quality or compression performance. We propose a new class of compression method by redefining the distance measure with a statistical concept known as exchangeability. This approach reduces the storage requirement and captures essential features, while reducing the storage requirement. In this paper, we report our design and implementation of such a compression method named IDEALEM. To demonstrate its e↵ectiveness, we apply it on a set of power grid monitoring data, and show that it can reduce the volume of data much more than the best known compression method while maintaining the quality of the compressed data. In these tests, IDEALEM captures extraordinary events in the data, while its compression ratios can far exceed 100.

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“…[12] . 의료 초음파 신호처리 분야에 서도 마찬가지로 B-mode 영상 처리에 있어서 압축 센 싱을 적용하여, 획득 시간을 개선하고 데이터 저장 공 간을 효율적으로 사용하고자 하는 연구가 지속적으로 이루어지고 있다 [13] . [14] .…”

Section: 주파수 영역에서의 감쇠 지수 예측 방법으로 Yao 등 은 감쇠 특성을 미리 알고 있는 참조(Referenceunclassified

Estimation of Ultrasonic Attenuation Coefficients in the Frequency Domain using Compressed Sensing

Shim¹,

Kim²

2016

Journal of the Institute of Electronics and Information Enginee

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Compressed Sensing(CS) is the theory that can recover signals which are sampled below the Nyquist sampling rate to original analog signals. In this paper, we propose the estimation algorithm of ultrasonic attenuation coefficients in the frequency domain using CS. While most estimation algorithms transform the time-domain signals into the frequency-domain using the Fourier transform, the proposed method directly utilize the spectral information in the recovery process by the basis matrix without the completely recovered signals in the time domain. We apply three transform bases for sparsifying and estimate the attenuation coefficients using the Centroid Downshift method with Dual-reference diffraction compensation technique. The estimation accuracy and execution time are compared for each basis matrix. Computer simulation results show that the DCT basis matrix exhibits less than 0.35% estimation error for the compressive ratio of 50% and about 6% average error for the compressive ratio of 70%. The proposed method which directly extracts frequency information from the CS signals can be extended to estimating for other ultrasonic parameters in the Quantitative Ultrasound (QUS) Analysis.

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Compressed sensing of ultrasound images: Sampling of spatial and frequency domains

Cited by 50 publications

References 12 publications

Bayesian compressed sensing in ultrasound imaging

Bayesian compressed sensing in ultrasound imaging

Novel Data Reduction Based on Statistical Similarity

Estimation of Ultrasonic Attenuation Coefficients in the Frequency Domain using Compressed Sensing

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