A Survey of Stochastic Simulation and Optimization Methods in Signal Processing

Pereyra, Marcelo; Schniter, Philip; Chouzenoux, Émilie; Pesquet, Jean-Christophe; Tourneret, Jean-Yves; Hero, Alfred O.; McLaughlin, Stephen

doi:10.1109/jstsp.2015.2496908

Cited by 119 publications

(126 citation statements)

References 130 publications

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“…The corresponding values of this regularization parameter are reported in Table I. At this point, it is worth mentioning that it would be interesting to consider approaches based on Bayesian inference [63] or on the Stein's unbiased risk estimate (SURE) [64] to estimate this regularization parameter. However, theses approaches are out of the scope of this paper.…”

Section: Dictionary Learning and Regularization Parametersmentioning

confidence: 99%

Motion Estimation in Echocardiography Using Sparse Representation and Dictionary Learning

Ouzir

Basarab

Liebgott

et al. 2018

IEEE Trans. on Image Process.

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OATAO is an open access repository that collects the work of Toulouse researchers and makes it freely available over the web where possible. Abstract-This paper introduces a new method for cardiac motion estimation in 2-D ultrasound images. The motion estimation problem is formulated as an energy minimization, whose data fidelity term is built using the assumption that the images are corrupted by multiplicative Rayleigh noise. In addition to a classical spatial smoothness constraint, the proposed method exploits the sparse properties of the cardiac motion to regularize the solution via an appropriate dictionary learning step. The proposed method is evaluated on one data set with available ground-truth, including four sequences of highly realistic simulations. The approach is also validated on both healthy and pathological sequences of in vivo data. We evaluate the method in terms of motion estimation accuracy and strain errors and compare the performance with state-of-the-art algorithms. The results show that the proposed method gives competitive results for the considered data. Furthermore, the in vivo strain analysis demonstrates that meaningful clinical interpretation can be obtained from the estimated motion vectors.

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Section: Dictionary Learning and Regularization Parametersmentioning

confidence: 99%

Motion Estimation in Echocardiography Using Sparse Representation and Dictionary Learning

Ouzir

Basarab

Liebgott

et al. 2018

IEEE Trans. on Image Process.

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“…Let γ > 0 and Q ∈ S n . Following [15], we replace π by its Moreau approximation π Q γ defined in (11) and recalled below,…”

Section: B Approximation Of the Target Diffusionmentioning

confidence: 99%

Preconditioned P-ULA for Joint Deconvolution-Segmentation of Ultrasound Images

Marie-Caroline

Kouamé

Chouzenoux

et al. 2019

IEEE Signal Process. Lett.

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Joint deconvolution and segmentation of ultrasound images is a challenging problem in medical imaging. By adopting a hierarchical Bayesian model, we propose an accelerated Markov chain Monte Carlo scheme where the tissue reflectivity function is sampled thanks to a recently introduced proximal unadjusted Langevin algorithm. This new approach is combined with a forward-backward step and a preconditioning strategy to accelerate the convergence, and with a method based on the majorizationminimization principle to solve the inner nonconvex minimization problems. As demonstrated in numerical experiments conducted on both simulated and in vivo ultrasound images, the proposed method provides high-quality restoration and segmentation results and is up to six times faster than an existing Hamiltonian Monte Carlo method.

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“…Stochastic optimization: SO uses random variables with random iterates to accelerate search [260]. It can solve both stochastic optimization problems and deterministic optimization problems.…”

Section: Optimizationmentioning

confidence: 99%

Pathological Brain Detection by Artificial Intelligence in Magnetic Resonance Imaging Scanning (Invited Review)

Wang¹,

Zhang²,

Zhan³

et al. 2016

PIER

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Abstract-(Aim) Pathological brain detection (PBD) systems aim to assist and even replace neuroradiologists to make decisions for patients. This review offers a comprehensive and quantitative comparison for PBD systems by artificial intelligence in magnetic resonance imaging (MRI) scanning. (Method) We first investigated four categories of brain diseases, including neoplastic disease, neurodegenerative disease, cerebrovascular disease, and inflammation. Next, we introduced important MRI techniques, such as the shimming, water and fat suppression, and three advanced imaging modalities (functional MRI, diffusion tensor imaging, and magnetic resonance spectroscopic imaging). Then, we discussed four image preprocessing techniques (image denoising, slice selection, brain extraction, spatial normalization, and intensity normalization), seven feature representation techniques (shape, moment, wavelet, statistics, entropy, gray level co-occurrence matrix, and Fourier transform), and two dimension reduction techniques (feature selection and feature extraction). Afterwards, we studied classification related methods: six learning models (decision tree, extreme learning machine, k-nearest neighbors, naive Bayes classifier, support vector machine, feed-forward neural network), five kernel functions (linear, homogeneous and inhomogeneous polynomial, radial basis function, and sigmoid), and three types of optimization methods (evolutionary algorithm, stochastic optimization, and swarm intelligence). (Results) We introduced three benchmark datasets and used K-fold stratified cross validation to avoid overfitting. We presented a detailed quantitative comparison among 44 state-of-theart PBD algorithms and discussed their advantages and limitations. (Discussions) Artificial intelligence is now making stride in the PBD field and enjoys a fair amount of success. In the future, semi-supervised learning and transfer learning techniques may be potential breakthroughs to develop PBD systems.

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A Survey of Stochastic Simulation and Optimization Methods in Signal Processing

Cited by 119 publications

References 130 publications

Motion Estimation in Echocardiography Using Sparse Representation and Dictionary Learning

Motion Estimation in Echocardiography Using Sparse Representation and Dictionary Learning

Preconditioned P-ULA for Joint Deconvolution-Segmentation of Ultrasound Images

Pathological Brain Detection by Artificial Intelligence in Magnetic Resonance Imaging Scanning (Invited Review)

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