Recent advances of variational model in medical imaging and applications to computer aided surgery

In this paper, we review some mathematical models in medical image processing. Due to the superiority in modeling and computation, variational methods have been proven to be powerful techniques, which have been extremely popular and dramatically improved in the past two decades. On one hand, many models have been proposed for nearly all kinds of applications. On the other hand, a lot of models can be globally optimized and also many computation tools have been introduced. Under the variational framework, we focus on two basic problems in medical imaging： image restoration and segmentation, which are core components for kinds of specific tasks. For image restoration, we discuss some models on both additive and multiplicative noises. For image segmentation, we review some models on both whole image segmentation and specific target delineation, with the later being a key step in computer aided surgery. Additionally, we present some models on liver delineation and give their applications to living donor liver transplantation.

Variational Modeling of Free-form Curve and Surface Using Wavelet Basis Functions

Variational modeling approach is often used to interactively design free-form curves and surfaces. Traditionally, a variational problem can be transformed to the optimization of control points. Unfortunately, as the number of basis functions grows, the local support property of B-spline often makes the computation of an optimization system time-consuming. To solve this problem, wavelet basis instead of B-spline basis is used to represent the curves or surfaces. Because the wavelet basis is a hierarchical basis with multiresolution property, the coarse wavelet basis can be used to describe the overall shape of the curves/surfaces, while the finer wavelet basis used to describe the details of the curves/surfaces. Thus, the computing speed of the optimization system can be raised greatly.

Variational Learned Talking-Head Semantic Coded Transmission System

Video transmission requires considerable bandwidth,and current widely employed schemes prove inadequate when confronted with scenes featuring prominently.Motivated by the strides in talkinghead generative technology,the paper introduces a semantic transmission system tailored for talking-head videos.The system captures semantic information from talking-head video and faithfully reconstructs source video at the receiver,only one-shot reference frame and compact semantic features are required for the entire transmission.Specifically,we analyze video semantics in the pixel domain frame-by-frame and jointly process multi-frame semantic information to seamlessly incorporate spatial and temporal information.Variational modeling is utilized to evaluate the diversity of importance among group semantics,thereby guiding bandwidth resource allocation for semantics to enhance system efficiency.The whole endto-end system is modeled as an optimization problem and equivalent to acquiring optimal rate-distortion performance.We evaluate our system on both reference frame and video transmission,experimental results demonstrate that our system can improve the efficiency and robustness of communications.Compared to the classical approaches,our system can save over 90%of bandwidth when user perception is close.

A Second-Order Image Denoising Model for Contrast Preservation

In this work,we propose a second-order model for image denoising by employing a novel potential function recently developed in Zhu(J Sci Comput 88:46,2021)for the design of a regularization term.Due to this new second-order derivative based regularizer,the model is able to alleviate the staircase effect and preserve image contrast.The augmented Lagrangian method(ALM)is utilized to minimize the associated functional and convergence analysis is established for the proposed algorithm.Numerical experiments are presented to demonstrate the features of the proposed model.

An Efficient Variational Model for Multiplicative Noise Removal

In this paper,an efficient variational model for multiplicative noise removal is proposed.By using a MAP estimator,Aubert and Aujol[SIAM J.Appl.Math.,68(2008),pp.925-946]derived a nonconvex cost functional.With logarithmic transformation,we transform the image into a logarithmic domain which makes the fidelity convex in the transform domain.Considering the TV regularization term in logarithmic domain may cause oversmoothness numerically,we propose the TV regularization directly in the original image domain,which preserves more details of images.An alternative minimization algorithm is applied to solve the optimization problem.The z-subproblem can be solved by a closed formula,which makes the method very efficient.The convergence of the algorithm is discussed.The numerical experiments show the efficiency of the proposed model and algorithm.

Evaluation and Control of Cyclic Variation in Spark Ignited Engines by a Thermodynamic Model

An evaluation method of engine cyclic variation is proposed based on fuzzy mathematics concept. The degree of engine cyclic variation is divided into 4 levels: stable, slight variation, moderate variation and serious variation based on the statistic standard deviation of residual gas temperatures within the specified simulation cycles and the function of cyclic variation is also inducted for the cyclic variation control. Because the degree of engine cyclic variation can be estimated qualitatively, the effective control means can be applied to appease the undesired cyclic variation. Simulation result shows that for a very serious cyclic variation through the proper adjustment of the spark angle and the cyclic variation will disappear.

Statistical Modeling of Gate Capacitance Variations Induced by Random Dopants in Nanometer MOSFETs Reserving Correlations

We consider intrinsic gate capacitance variations due to random dopants in the nanometer metal oxide semi- conductor field effect transistor （MOSFET） channel. The variations of total gate capacitance and gate transcapacitances are investigated and the strong correlations between the trans-capacitance variations are discovered. A simple statistical model is proposed for accurately capturing total gate capacitance variability based on the correlations. The model fits very well with the Monte Carlo simulations and the average errors are -0.033% for n-type metal-oxide semiconductor and -0.012% for p-type metal-oxide semiconductor, respectively. Our simulation studies also indicate that, owing to these correlations, the total gate capacitance variability will not dominate in gate capacitance variations.

Numerical modelling of the depth variation and the fluctuation of the thermocline under the effects of wind in the Bohai Sea

-In this paper, numerical modelling of the fluctuation of the thermocline in the Bohai Sea has been made using a two-dimensional nonlinear model in stratified ocean and the model for the depth of the thermocline under the effects of wind stirring. The computed results depict the variations of the fluctuation of the thermocline driven by different kinds of wind fields. The fluctuation of the thermocline in the Bohai Sea varies somewhat with different directions, paths and locations of typhoon (cyclone). Under the effects of strong wind, the thermoclines both sink due to mixing and fluctuate. Furthermore, the fluctuation of the thermocline speeds up mixing. At last, the thermoclines disappear after 12-15 h when the strong wind increases from Force 6 to Force 9.

Pressure dissociation of dense hydrogen

The self-consistent fluid variational model （SFVM） has been used to describe the pressure dissociation of dense hydrogen at high temperatures. This paper focuses on a mixture of hydrogen atoms and molecules and is devoted to the study of the phenomenon of pressure dissociation at finite temperatures. The equation of state and dissociation degree have been calculated from the free energy functions in the range of temperature 2000-10,000K and density 0.02-1.0g/cm^3, which can be compared with other approaches and experiments. The pressure dissociation is found to occur in higher density range, while temperature dissociation is a more gradual effect.

Noise Reduction of Welding Defect Image Based on NSCT and Anisotropic Diffusion

In order to reduce noise effectively in the welding defect image and preserve the minutiae information, a noise reduction method of welding defect image based on nonsubsampled contourlet transform(NSCT) and anisotropic diffusion is proposed. Firstly, an X-ray welding defect image is decomposed by NSCT. Then total variation(TV) model and Catte_PM model are used for the obtained low-pass component and band-pass components, respectively. Finally, the denoised image is synthesized by inverse NSCT. Experimental results show that, compared with the hybrid method of wavelet threshold shrinkage with TV diffusion, the method combining NSCT with P_Laplace diffusion, and the method combining contourlet with TV model and adaptive contrast diffusion, the proposed method has a great improvement in the aspects of subjective visual effect, peak signal-to-noise ratio(PSNR) and mean-square error(MSE). Noise is suppressed more effectively and the minutiae information is preserved better in the image.

Traffic assignment problem under tradable credit scheme in a bi-modal stochastic transportation network: A cumulative prospect theory approach

The traffic equilibrium assignment problem under tradable credit scheme(TCS) in a bi-modal stochastic transportation network is investigated in this paper. To describe traveler’s risk-taking behaviors under uncertainty, the cumulative prospect theory(CPT) is adopted. Travelers are assumed to choose the paths with the minimum perceived generalized path costs, consisting of time prospect value(PV) and monetary cost. At equilibrium with a given TCS, the endogenous reference points and credit price remain constant, and are consistent with the equilibrium flow pattern and the corresponding travel time distributions of road sub-network. To describe such an equilibrium state, the CPT-based stochastic user equilibrium(SUE) conditions can be formulated under TCS. An equivalent variational inequality(VI) model embedding a parameterized fixed point(FP) model is then established, with its properties analyzed theoretically. A heuristic solution algorithm is developed to solve the model, which contains two-layer iterations. The outer iteration is a bisection-based contraction method to find the equilibrium credit price, and the inner iteration is essentially the method of successive averages(MSA) to determine the corresponding CPT-based SUE network flow pattern. Numerical experiments are provided to validate the model and algorithm.

An Effective Strip Noise Removal Method for Remote Sensing Image

In this paper,an efficient technique for removing strip noise from remote sensing images is proposed in order to better retain image details.Firstly,the remote sensing image with strip noise is decomposed by wavelet technology;Secondly,two variational models are constructed,stripe preserve variation model and a destriping variation model.In order to efficiently separate the detail information in the low level high-frequency component,the stripe preserve variation model eliminates the detail information from the low level high-frequency component(including strip noise)while maintaining the strip noise(including strip noise).In order to successfully save the details in the high level high-frequency component,the destriping variation model eliminates the strip noise in the high level high-frequency component(including the strip noise).Finally,wavelet reconstruction is used to get the denoised image.It is clear from a comparison with previous approaches that the suggested method not only successfully removes strip noise but also preserves image details.

Stochastic Variational Inference-Based Parallel and Online Supervised Topic Model for Large-Scale Text Processing

Topic modeling is a mainstream and effective technology to deal with text data, with wide applications in text analysis, natural language, personalized recommendation, computer vision, etc. Among all the known topic models, supervised Latent Dirichlet Allocation （sLDA） is acknowledged as a popular and competitive supervised topic model. How- ever, the gradual increase of the scale of datasets makes sLDA more and more inefficient and time-consuming, and limits its applications in a very narrow range. To solve it, a parallel online sLDA, named PO-sLDA （Parallel and Online sLDA）, is proposed in this study. It uses the stochastic variational inference as the learning method to make the training procedure more rapid and efficient, and a parallel computing mechanism implemented via the MapReduce framework is proposed to promote the capacity of cloud computing and big data processing. The online training capacity supported by PO-sLDA expands the application scope of this approach, making it instrumental for real-life applications with high real-time demand. The validation using two datasets with different sizes shows that the proposed approach has the comparative accuracy as the sLDA and can efficiently accelerate the training procedure. Moreover, its good convergence and online training capacity make it lucrative for the large-scale text data analyzing and processing.

Reliability analysis of stochastic park-and-ride network

Park-and-ride （P＆R） facilities can alleviate the traffic burden in central urban areas by enabling car drivers to park at the perimeter of congested areas and continue their journeys with public transportation （e.g., metro and bus rapid transit）. Whether a P＆R scheme is successful depends on its attractiveness to car users. This paper presents anevaluation method for the reliability analysis of P＆R mode. Two indices, P＆R reliability and mode reliability, are in- troduced to represent the reliabilities of a transfer point and an entire trip, respectively. Then, a systematic reliability analysis is conducted for a stochastic P＆R network, where travelers can complete their journeys via two options： auto mode or P＆R mode. A variational inequality （VI） model is proposed and solved by a heuristic solution algorithm. Nu- merical results show that the P＆R facility reliability is significantly influenced by the capacity of parking facilities, the dispatching frequency of the connecting metro, and the metro fare. In addition, a higher level of total demand in the network has significant negative impacts on P＆R mode＇s attractiveness compared to auto mode.

IMAGE SUPER-RESOLUTION RECONSTRUCTION BY HUBER REGULARIZATION AND TAILORED FINITE POINT METHOD

In this paper,we propose using the tailored finite point method(TFPM)to solve the resulting parabolic or elliptic equations when minimizing the Huber regularization based image super-resolution model using the augmented Lagrangian method(ALM).The Hu-ber regularization based image super-resolution model can ameliorate the staircase for restored images.TFPM employs the method of weighted residuals with collocation tech-nique,which helps get more accurate approximate solutions to the equations and reserve more details in restored images.We compare the new schemes with the Marquina-Osher model,the image super-resolution convolutional neural network(SRCNN)and the classical interpolation methods:bilinear interpolation,nearest-neighbor interpolation and bicubic interpolation.Numerical experiments are presented to demonstrate that with the new schemes the quality of the super-resolution images has been improved.Besides these,the existence of the minimizer of the Huber regularization based image super-resolution model and the convergence of the proposed algorithm are also established in this paper.

MULTIGRID METHOD FOR A MODIFIED CURVATURE DRIVEN DIFFUSION MODEL FOR IMAGE INPAINTING

Digital inpainting is a fundamental problem in image processing and many variational models for this problem have appeared recently in the literature. Among them are the very successfully Total Variation （TV） model [11] designed for local inpainting and its improved version for large scale inpainting： the Curvature-Driven Diffusion （CDD） model [10]. For the above two models, their associated Euler Lagrange equations are highly nonlinear partial differential equations. For the TV model there exists a relatively fast and easy to implement fixed point method, so adapting the multigrid method of [24] to here is immediate. For the CDD model however, so far only the well known but usually very slow explicit time marching method has been reported and we explain why the implementation of a fixed point method for the CDD model is not straightforward. Consequently the multigrid method as in [Savage and Chen, Int. J. Comput. Math., 82 （2005）, pp. 1001-1015] will not work here. This fact represents a strong limitation to the range of applications of this model since usually fast solutions are expected. In this paper, we introduce a modification designed to enable a fixed point method to work and to preserve the features of the original CDD model. As a result, a fast and efficient multigrid method is developed for the modified model. Numerical experiments are presented to show the very good performance of the fast algorithm.

Variational reconstruction using subdivision surfaces with continuous sharpness control

We present a variational method for subdivision surface reconstruction from a noisy dense mesh. A new set of subdivision rules with continuous sharpness control is introduced into Loop subdivision for better modeling subdivision surface features such as semi-sharp creases, creases, and corners. The key idea is to assign a sharpness value to each edge of the control mesh to continuously control the surface features.Based on the new subdivision rules, a variational model with L_1 norm is formulated to find the control mesh and the corresponding sharpness values of the subdivision surface that best fits the input mesh. An iterative solver based on the augmented Lagrangian method and particle swarm optimization is used to solve the resulting non-linear, non-differentiable optimization problem. Our experimental results show that our method can handle meshes well with sharp/semi-sharp features and noise.

Efficient Dual Algorithms for Image Segmentation Using TV-Allen-Cahn Type Models

Variational image segmentation based on the Mumford and Shah model[31],together with implementation by the piecewise constant level-set method(PCLSM)[26],leads to fully nonlinear Total Variation(TV)-Allen-Cahn equations.The commonlyused numerical approaches usually suffer from the difficulties not only with the nondifferentiability of the TV-term,but also with directly evolving the discontinuous piecewise constant-structured solutions.In this paper,we propose efficient dual algorithms to overcome these drawbacks.The use of a splitting-penalty method results in TVAllen-Cahn type models associated with different"double-well"potentials,which allow for the implementation of the dual algorithm of Chambolle[8].Moreover,we present a new dual algorithm based on an edge-featured penalty of the dual variable,which only requires to solve a vectorial Allen-Cahn type equation with linear∇(div)-diffusion rather than fully nonlinear diffusion in the Chambolle’s approach.Consequently,more efficient numerical algorithms such as time-splitting method and Fast Fourier Transform(FFT)can be implemented.Various numerical tests show that two dual algorithms are much faster and more stable than the primal gradient descent approach,and the new dual algorithm is at least as efficient as the Chambolle’s algorithm but is more accurate.We demonstrate that the new method also provides a viable alternative for image restoration.

Evolution Model Based on Prior Information for Narrow Joint Segmentation

Automated segmentation of hip joint computed tomography images is significantly important in the diagnosis and treatment of hip joint disease.In this paper,we propose an automatic hip joint segmentation method based on a variational model guided by prior information.In particular,we obtain prior features by automatic sample selection,get a discriminative function by training these selected samples and then integrate this prior information into our variational model.Numerical results demonstrate that the proposed method has high accuracy in segmenting narrow joint regions.

A Mean Curvature Regularized Based Model for Demodulating Phase Maps from Fringe Patterns

We introduce a variationalmethod for demodulating phasemaps fromfringe patterns.This newmethod is based on themean curvature of the level sets of the phase surface that is used for regularization.The performance of the method is illustrated with both synthetic and real data.