stacs new active contour scheme for cardiac mr image segmentation the paper presents a novel stochastic active contour scheme stacs for automatic image segmentation designed to overcome some of the unique challenges in cardiac mr images such as problems with low contrast papillary muscles and turbulent blood flow stacs minimizes an energy functional that combines stochastic region based and edge based information with shape priors of the heart and local properties of the contour the minimization algorithm solves by the level set method the euler lagrange equation that describes the contour evolution stacs includes an annealing schedule that balances dynamically the weight of the different terms in the energy functional three particularly attractive features of stacs are 1 ability to segment images with low texture contrast by modeling stochastically the image textures 2 robustness to initial contour and noise because of the utilization of both edge and region based information 3 ability to segment the heart from the chest wall and the undesired papillary muscles due to inclusion of heart shape priors application of stacs to a set of 48 real cardiac mr images shows that it can successfully segment the heart from its surroundings such as the chest wall and the heart structures the left and right ventricles and the epicardium we compare stacs automatically generated contours with manually traced contours or the gold standard using both area and edge similarity measures this assessment demonstrates very good and consistent segmentation performance of stacs

a region and gradient based active contour model and its application in boundary tracking on anal canal ultrasound images a novel gaussian mixture model gmm based region and gradient active contour model is proposed for general object boundary tracking and the purpose of boundary tracking for anal muscle layers motion information is extracted from adjacent slice and is used to guide the first step of boundary tracking procedure the idea is that gmm is introduced into the statistical feature computation for object region and background region thereby providing an accurate model for regional pixel intensity description expectation maximization algorithm and k means algorithm are used for parameter solutions of gmm based on the available region information gradient information and the self constraints of the contour a unifying active contour model is proposed the proposed active contour models and tracking algorithm were tested on synthetic images and simulated ultrasound images to evaluate some generic features of the model for boundary tracking furthermore it was applied to perform boundary tracking of anal muscle layers the tracking results were evaluated by three experts the results showed that the proposed method has a good performance for boundary tracking on anal wall ultrasound image

direct noise simulation of a canonical high lift device and comparison with an analytical model the noise of a canonical main element flap high lift device hld is computed directly using compressible wall resolved large eddy simulation an experimental database for the chosen configuration allows us to successfully validate the chosen numerical approach both the noise sources and the far field acoustic pressure are shown to be well predicted although the two elements trailing edge noise can be observed in the near field the flap remains as the dominant source in the far field the simplicity of the studied configuration enables the comparison of the validated numerical results with a recently developed analytical model that takes into account the diffraction of the flap noise by the main element a two dimensional 2d with and without kutta condition and a three dimensional without kutta correction analytical formulations are compared with the numerical results all formulations compare favorably with the numerical reference in terms of noise levels and directivities however the 2d formulation with a kutta correction provides the best quantitative agreement as expected from the narrow span of the numerical domain the recently developed analytical model is therefore a good predictive tool for hld showing that it can properly account for the diffraction effect of the main element on the flap main noise source

parametric surface diffeomorphometry for low dimensional embeddings of dense segmentations and imagery in the field of computational anatomy biological form including our focus neuroanatomy is studied quantitatively through the action of the diffeomorphism group on example anatomies a technique called diffeomorphometry here we design an algorithm within this framework to pass from dense objects common in neuromaging studies binary segmentations structural images to a sparse representation defined on the surface boundaries of anatomical structures and embedded into the low dimensional coordinates of a parametric model our main new contribution is to introduce an expanded group action to simultaneously deform surfaces through direct mapping of points as well as images through functional composition with the inverse this allows us to index the diffeomorphisms with respect to two dimensional surface geometries like subcortical gray matter structures but explicitly map onto cost functions determined by noisy 3 dimensional measurements we consider models generated from empirical covariance of training data as well as bandlimited laplace beltrami eigenfunction models when no such data is available we show applications to noisy or anomalous segmentations and other typical problems in neuroimaging studies we reproduce statistical results detecting changes in alzheimer s disease despite dimensionality reduction lastly we apply our algorithm to the common problem of segmenting subcortical structures from t1 mr images

dynamic 3d models with local and global deformations deformable superquadrics the authors present a physically based approach to fitting complex three dimensional shapes using a novel class of dynamic models that can deform both locally and globally they formulate the deformable superquadrics which incorporate the global shape parameters of a conventional superellipsoid with the local degrees of freedom of a spline the model s six global deformational degrees of freedom capture gross shape features from visual data and provide salient part descriptors for efficient indexing into a database of stored models the local deformation parameters reconstruct the details of complex shapes that the global abstraction misses the equations of motion which govern the behavior of deformable superquadrics make them responsive to externally applied forces the authors fit models to visual data by transforming the data into forces and simulating the equations of motion through time to adjust the translational rotational and deformational degrees of freedom of the models model fitting experiments involving 2d monocular image data and 3d range data are presented

a fast level set method for propagating interfaces a method is introduced to decrease the computational labor of the standard level set method for propagating interfaces the fast approach uses only points close to the curve at every time step we describe this new algorithm and compare its efficiency and accuracy with the standard level set approach

reconstruction of turbulent fluctuations using a hybrid rans les approach a hybrid rans les approach is presented and assessed considering several flows which can be interpreted as the most general case of the nlde approach as defined by morris et al p j morris l n long a bangalore and a wang j comput phys 133 56 1997 a decomposition into three parts of the exact solution of the navier stokes equations is considered mean flow resolved fluctuations and unresolved subgrid fluctuations the mean flow is computed using a classical rans method while resolved fluctuations are derived from a les method this approach is succesfully assessed on the stationary and the pulsed plane channel flow configurations the case of the flow around a low pressure turbine blade is discussed in a second step the hybrid method is also demonstrated to be robust with respect to several sources of error and provides good results in the case of a coarsened grid simulation a reduced computational domain simulation and nonconsistent mean flow fields

interactive image segmentation based on synthetic graph coordinates in this paper we propose a framework for interactive image segmentation the goal of interactive image segmentation is to classify the image pixels into foreground and background classes when some foreground and background markers are given the proposed method minimizes a min max bayesian criterion that has been successfully used on image segmentation problem and it consists of several steps in order to take into account visual information as well as the given markers without any requirement of training first we partition the image into contiguous and perceptually similar regions superpixels then we construct a weighted graph that represents the superpixels and the connections between them an efficient algorithm for graph clustering based on synthetic coordinates is used yielding an initial map of classified pixels this method reduces the problem of graph clustering to the simpler problem of point clustering instead of solving the problem on the graph data structure as most of the known algorithms from literature do finally having available the data modeling and the initial map of classified pixels we use a markov random field mrf model or a flooding algorithm to get the image segmentation by minimizing a min max bayesian criterion experimental results and comparisons with other methods from the literature are presented on lhi gulshan and zhao datasets demonstrating the high performance and accuracy of the proposed scheme

local optimization based segmentation of spatially recurring multi region objects with part configuration constraints incorporating prior knowledge into image segmentation algorithms has proven useful for obtaining more accurate and plausible results two important constraints containment and exclusion of regions have gained attention in recent years mainly due to their descriptive power in this paper we augment the level set framework with the ability to handle these two intuitive geometric relationships containment and exclusion along with a distance constraint between boundaries of multi region objects level set s important property of automatically handling topological changes of evolving contours surfaces enables us to segment spatially recurring objects e g multiple instances of multi region cells in a large microscopy image while satisfying the two aforementioned constraints in addition the level set approach gives us a very simple and natural way to compute the distance between contours surfaces and impose constraints on it the downside however is a local optimization framework in which the final segmentation solution depends on the initialization in fact here we sacrifice the optimizability local instead of global solution in exchange for lower space complexity less memory usage and faster runtime especially for large microscopic images as well as no grid artifacts nevertheless the result from validating our method on several biomedical applications showed the utility and advantages of this augmented level set framework even with rough initialization that is distant from the desired boundaries we also compared our framework with its counterpart methods in the discrete domain and reported the pros and cons of each of these methods in terms of metrication error and efficiency in memory usage and runtime

a geometric model for active contours in image processing we propose a new model for active contours based on a geometric partial differential equation our model is intrinsec stable satisfies the maximum principle and permits a rigorous mathematical analysis it enables us to extract smooth shapes we cannot retrieve angles and it can be adapted to find several contours simultaneously moreover as a consequence of the stability we can design robust algorithms which can be engineed with no parameters in applications numerical experiments are presented

volumetric transformation of brain anatomy presents diffeomorphic transformations of three dimensional 3 d anatomical image data of the macaque occipital lobe and whole brain cryosection imagery and of deep brain structures in human brains as imaged via magnetic resonance imagery these transformations are generated in a hierarchical manner accommodating both global and local anatomical detail the initial low dimensional registration is accomplished by constraining the transformation to be in a low dimensional basis the basis is defined by the green s function of the elasticity operator placed at predefined locations in the anatomy and the eigenfunctions of the elasticity operator the high dimensional large deformations are vector fields generated via the mismatch between the template and target image volumes constrained to be the solution of a navier stokes fluid model as part of this procedure the jacobian of the transformation is tracked insuring the generation of diffeomorphisms it is shown that transformations constrained by quadratic regularization methods such as the laplacian biharmonic and linear elasticity models do not ensure that the transformation maintains topology and therefore must only be used for coarse global registration

reynolds averaged navier stokes large eddy simulations of supersonic base flow several zonal and nonzonal hybrid reynolds averaged navier stokes large eddy simulation approaches have been assessed to handle a high reynolds number supersonic base flow the results obtained on 5 and 13 5 x 10 6 points grids are compared to the available experimental data herrin j l and dutton j c supersonic base flow experiments in the near wake of a cylindrical afterbody aiaa journal vol 32 no 77 1994 and the capabilities of these different methodologies to predict supersonic flows are discussed the highly compressible separated shear layer proved to be a challenging issue for hybrid methods due to an alteration in the instability process as compared to the incompressible case leading to three dimensional coherent structures numerous numerical parameters relevant to hybrid methods have been assessed the incoming boundary layer thickness needs to be properly set while a weak influence of the subgrid scale model is observed when small scale structures in the separating mixing layer are resolved another finding of the present study is the dramatic influence displayed by the numerical dissipation on the flowfield sensitivity to the c des model constant is observed even with the finest grid leading to a delay in the generation of instabilities in the shear layer

snakes active contour models a snake is an energy minimizing spline guided by external constraint forces and influenced by image forces that pull it toward features such as lines and edges snakes are active contour models they lock onto nearby edges localizing them accurately scale space continuation can be used to enlarge the capture region surrounding a feature snakes provide a unified account of a number of visual problems including detection of edges lines and subjective contours motion tracking and stereo matching we have used snakes successfully for interactive interpretation in which user imposed constraint forces guide the snake near features of interest

a multiphase level set framework for image segmentation using the mumford and shah model we propose a new multiphase level set framework for image segmentation using the mumford and shah model for piecewise constant and piecewise smooth optimal approximations the proposed method is also a generalization of an active contour model without edges based 2 phase segmentation developed by the authors earlier in t chan and l vese 1999 in scale space 99 m nilsen et al eds lncs vol 1682 pp 141 151 and t chan and l vese 2001 ieee ip 10 2 266 277 the multiphase level set formulation is new and of interest on its own by construction it automatically avoids the problems of vacuum and overlaps it needs only log n level set functions for n phases in the piecewise constant cases it can represent boundaries with complex topologies including triple junctionss in the piecewise smooth case only two level set functions formally suffice to represent any partition based on the four color theorem finally we validate the proposed models by numerical results for signal and image denoising and segmentation implemented using the osher and sethian level set method

the mean field limit for solid particles in a navier stokes flow we propose a mathematical derivation of brinkman s force for a cloud of particles immersed in an incompressible viscous fluid specifically we consider the stokes or steady navier stokes equations in a bounded domain 3 for the velocity field u of an incompressible fluid with kinematic viscosity and density 1 brinkman s force consists of a source term 6 j where j is the current density of the particles and of a friction term 6 u where is the number density of particles these additional terms in the motion equation for the fluid are obtained from the stokes or steady navier stokes equations set in minus the disjoint union of n balls of radius e 1 n in the large n limit with no slip boundary condition the number density and current density j are obtained from the limiting phase space empirical measure frac 1 n sum 1 le k le n delta x k v k where xk is the center of the k th ball and vk its instantaneous velocity this can be seen as a generalization of allaire s result in arch ration mech anal 113 209 259 1991 who considered the case of periodically distributed xks with vk 0 and our proof is based on slightly simpler though similar homogenization arguments similar equations are used for describing the fluid phase in various models for sprays

using prior shapes in geometric active contours in a variational framework in this paper we report an active contour algorithm that is capable of using prior shapes the energy functional of the contour is modified so that the energy depends on the image gradient as well as the prior shape the model provides the segmentation and the transformation that maps the segmented contour to the prior shape the active contour is able to find boundaries that are similar in shape to the prior even when the entire boundary is not visible in the image i e when the boundary has gaps a level set formulation of the active contour is presented the existence of the solution to the energy minimization is also established we also report experimental results of the use of this contour on 2d synthetic images ultrasound images and fmri images classical active contours cannot be used in many of these images

homogenization of the navier stokes equations in open sets perforated with tiny holes i abstract framework a volume distribution of holes this paper treats the homogenization of the stokes or navier stokes equations with a dirichlet boundary condition in a domain containing many tiny solid obstacles periodically distributed in each direction of the axes for example in the three dimensional case the obstacles have a size of e3 and are located at the nodes of a regular mesh of size e a suitable extension of the pressure is used to prove the convergence of the homogenization process to a brinkman type law in which a linear zero order term for the velocity is added to a stokes or navier stokes equation

using canny s criteria to derive a recursively implemented optimal edge detector a highly efficient recursive algorithm for edge detection is presented using canny s design 1 we show that a solution to his precise formulation of detection and localization for an infinite extent filter leads to an optimal operator in one dimension which can be efficiently implemented by two recursive filters moving in opposite directions in addition to the noise truncature immunity which results the recursive nature of the filtering operations leads with sequential machines to a substantial saving in computational effort five multiplications and five additions for one pixel independent of the size of the neighborhood the extension to the two dimensional case is considered and the resulting filtering structures are implemented as two dimensional recursive filters hence the filter size can be varied by simply changing the value of one parameter without affecting the time execution of the algorithm performance measures of this new edge detector are given and compared to canny s filters various experimental results are shown

embedding overlap priors in variational left ventricle tracking we propose to embed overlap priors in variational tracking of the left ventricle lv in cardiac magnetic resonance mr sequences the method consists of evolving two curves toward the lv endo and epicardium boundaries we derive the curve evolution equations by minimizing two functionals each containing an original overlap prior constraint the latter measures the conformity of the overlap between the nonparametric kernel based intensity distributions within the three target regions lv cavity myocardium and background to a prior learned from a given segmentation of the first frame the bhattacharyya coefficient is used as an overlap measure different from existing intensity driven constraints the proposed priors do not assume implicitly that the overlap between the intensity distributions within different regions has to be minimal this prevents both the papillary muscles from being included erroneously in the myocardium and the curves from spilling into the background although neither geometric training nor preprocessing were used quantitative evaluation of the similarities between automatic and independent manual segmentations showed that the proposed method yields a competitive score in comparison with existing methods this allows more flexibility in clinical use because our solution is based only on the current intensity data and consequently the results are not bounded to the characteristics variability and mathematical description of a finite training set we also demonstrate experimentally that the overlap measures are approximately constant over a cardiac sequence which allows to learn the overlap priors from a single frame

on the macroscopic description of slow viscous flow past a random array of spheres we give rigorous derivation of brinkman s equation as the effective equation for slow viscous flow in porous media with high porosity the medium is composed of spherical obstacles distributed randomly and the microscopic flow is described by the stokes equation along the road we givew21 convergence estimates for the point sources approximation

zonal immersed boundary conditions application to a high reynolds number afterbody flow the present paper focuses on a new numerical strategy consisting of the zonal use of the immersed boundary conditions combined with the ability of zonal detached eddy simulation to simulate high reynolds number separated flows the validation test case is a simplified space launcher afterbody with a control device in the form of a short cylindrical serrated skirt zonal immersed boundary conditions are used to handle the introduction of the skirt into the preexisting structured curvilinear grid that describes the noncontrolled configuration the governing equations are solved using a standard body fitted finite volume technique over the whole grid a direct forcing source term is added when cells are internal to the skirt that is solid to drive the velocity and the turbulence variables to the chosen values numerical simulations are performed at a reynolds number of 1 2 106 and a freestream mach number of 0 702 the numerical results demonstrate the ability of the zonal immersed boundary conditions t

an efficient optimization framework for multi region segmentation based on lagrangian duality we introduce a multi region model for simultaneous segmentation of medical images in contrast to many other models geometric constraints such as inclusion and exclusion between the regions are enforced which makes it possible to correctly segment different regions even if the intensity distributions are identical we efficiently optimize the model using a combination of graph cuts and lagrangian duality which is faster and more memory efficient than current state of the art as the method is based on global optimization techniques the resulting segmentations are independent of initialization we apply our framework to the segmentation of the left and right ventricles myocardium and the left ventricular papillary muscles in magnetic resonance imaging and to lung segmentation in full body x ray computed tomography we evaluate our approach on a publicly available benchmark with competitive results

on an elaboration of m kac s theorem concerning eigenvalues of the laplacian in a region with randomly distributed small obstacles we removem balls of centersw 1 w m with the same radius m from a bounded domain inr 3 with smooth boundary let k m w m denote thek th eigenvalue of the laplacian in m balls under the dirichlet condition we consider k m w m as a random variable on a probability space w 1 w m and we examine a precise behaviour of k m w m asm we give an elaboration of m kac s theorem

a level set approach for shape driven segmentation and tracking of the left ventricle knowledge based segmentation has been explored significantly in medical imaging prior anatomical knowledge can be used to define constraints that can improve performance of segmentation algorithms to physically corrupted and incomplete data in this paper the objective is to introduce such knowledge based constraints while preserving the ability of dealing with local deformations toward this end we propose a variational level set framework that can account for global shape consistency as well as for local deformations in order to improve performance the problems of segmentation and tracking of the structure of interest are dealt with simultaneously by introducing the notion of time in the process and looking for a solution that satisfies that prior constraints while being consistent along consecutive frames promising experimental results in magnetic resonance and ultrasonic cardiac images demonstrate the potentials of our approach

tracking using motion estimation with physically motivated inter region constraints we propose a method for tracking structures e g ventricles and myocardium in cardiac images e g magnetic resonance by propagating forward in time a previous estimate of the structures using a new physically motivated motion estimation scheme our method estimates motion by regularizing only within structures so that differing motions among different structures are not mixed it simultaneously satisfies the physical constraints at the interface between a fluid and a medium that the normal component of the fluid s motion must match the normal component of the medium s motion and the no slip condition which states that the tangential velocity approaches zero near the interface we show that these conditions lead to partial differential equations with robin boundary conditions at the interface which couple the motion between structures we show that propagating a segmentation across frames using our motion estimation scheme leads to more accurate segmentation than traditional motion estimation that does not use physical constraints our method is suited to interactive segmentation prominently used in commercial applications for cardiac analysis where segmentation propagation is used to predict a segmentation in the next frame we show that our method leads to more accurate predictions than a popular and recent interactive method used in cardiac segmentation

reynolds averaged navier stokes computations of a flap side edge flowfield an extensive computational investigation of a generic high lift configuration comprising a wing and a half span flap reveals details of the mean flow field for flap deflections of 29 and 39 degrees the computational effort involves solutions of the thin layer form of the reynolds averaged navier stokes rans equations for both flap deflections the steady results show the presence of a dualvortex system a strong vortex forming on the lower portion of the flap side edge and a weaker one forming near the edge on the flap top surface downstream the vortex on the flap side edge grows and eventually merges with the vortex on the flap top surface comparison of on and off surface flow quantities with the experimental measurements of radeztsky singer and khorrami aiaa paper 98 0700 show remarkable agreement for the 39 degree flap deflection the calculation also reveals the occurrence of a vortex breakdown which is corroborated by 5 hole probe velocity measurements performed in the quiet flow facility at nasa langley the presence of the vortex breakdown significantly alters the flow field near the side edge

combining multiple dynamic models and deep learning architectures for tracking the left ventricle endocardium in ultrasound data we present a new statistical pattern recognition approach for the problem of left ventricle endocardium tracking in ultrasound data the problem is formulated as a sequential importance resampling algorithm such that the expected segmentation of the current time step is estimated based on the appearance shape and motion models that take into account all previous and current images and previous segmentation contours produced by the method the new appearance and shape models decouple the affine and nonrigid segmentations of the left ventricle to reduce the running time complexity the proposed motion model combines the systole and diastole motion patterns and an observation distribution built by a deep neural network the functionality of our approach is evaluated using a dataset of diseased cases containing 16 sequences and another dataset of normal cases comprised of four sequences where both sets present long axis views of the left ventricle using a training set comprised of diseased and healthy cases we show that our approach produces more accurate results than current state of the art endocardium tracking methods in two test sequences from healthy subjects using three test sequences containing different types of cardiopathies we show that our method correlates well with interuser statistics produced by four cardiologists

localization of shapes using statistical models and stochastic optimization in this paper we present a new model for deformations of shapes a pseudolikelihood is based on the statistical distribution of the gradient vector field of the gray level the prior distribution is based on the probabilistic principal component analysis ppca we also propose a new model based on mixtures of ppca that is useful in the case of greater variability in the shape a criterion of global or local object specificity based on a preliminary color segmentation of the image is included into the model the localization of a shape in an image is then viewed as minimizing the corresponding gibbs field we use the exploration selection e s stochastic algorithm in order to find the optimal deformation this yields a new unsupervised statistical method for localization of shapes in order to estimate the statistical parameters for the gradient vector field of the gray level we use an iterative conditional estimation ice procedure the color segmentation of the image can be computed with an exploration selection estimation ese procedure

prediction of high lift flows using turbulent closure models the flow over two different multi element airfoil configurations is computed using linear eddy viscosity turbulence models and a nonlinear explicit algebraic stress model a subset of recently measured transition locations using hot film on a mcdonnell douglas configuration is presented and the effect of transition location on the computed solutions is explored deficiencies in wake profile computations are found to be attributable in large part to poor boundary layer prediction on the generating element and not necessarily inadequate turbulence modeling in the wake using measured transition locations for the main element improves the prediction of its boundary layer thickness skin friction and wake profile shape however using measured transition locations on the slat still yields poor slat wake predictions the computation of the slat flow field represents a key roadblock to successful predictions of multi element flows in general the nonlinear explicit algebraic stress turbulence model gives very similar results to the linear eddy viscosity models

region competition unifying snakes region growing and bayes mdl for multiband image segmentation we present a novel statistical and variational approach to image segmentation based on a new algorithm named region competition this algorithm is derived by minimizing a generalized bayes minimum description length mdl criterion using the variational principle the algorithm is guaranteed to converge to a local minimum and combines aspects of snakes balloons and region growing the classic snakes balloons and region growing algorithms can be directly derived from our approach we provide theoretical analysis of region competition including accuracy of boundary location criteria for initial conditions and the relationship to edge detection using filters it is straightforward to generalize the algorithm to multiband segmentation and we demonstrate it on gray level images color images and texture images the novel color model allows us to eliminate intensity gradients and shadows thereby obtaining segmentation based on the albedos of objects it also helps detect highlight regions

a dynamic forcing method for unsteady turbulent inflow conditions the present paper aims to provide an efficient and flexible method for the initialization of a zonal rans les type calculation when the resolution of the near wall region is treated in rans mode indeed when part of the boundary layer must be resolved in les mode one generally experiences a very long transient state which makes this approach inapplicable to industrial applications the skillful combination of a zonal detached eddy simulation method zdes a synthetic eddy method sem and a self adaptative dynamic forcing approach enables this the two former being taken as framework while the latter based on innovative considerations is the purpose of the paper the main strength of the dynamic forcing method comes from its local nature enabling to treat geometrically complex applications a new definition of the dynamic forcing method based on v 2 t error is derived from the original one it dramatically increases the efficiency of the inflow generation indeed the dynamic forcing method allows to reduce the transition distance up to 76 compared to the sem inflow by itself when a rans les type resolution is employed thus the use of a synthetic turbulence generation method is now affordable for industrial applications both technically and economically a particular attention is brought to the behavior and the parametrization of such an approach with regards to the other simulation parameters the authors will try to give all the information required to successfully apply the present strategy on a particular case

improved myocardial motion estimation combining tissue doppler and b mode echocardiographic images we propose a technique for myocardial motion estimation based on image registration using both b mode echocardiographic images and tissue doppler sequences acquired interleaved the velocity field is modeled continuously using b splines and the spatiotemporal transform is constrained to be diffeomorphic images before scan conversion are used to improve the accuracy of the estimation the similarity measure includes a model of the speckle pattern distribution of b mode images it also penalizes the disagreement between tissue doppler velocities and the estimated velocity field registration accuracy is evaluated and compared to other alternatives using a realistic synthetic dataset obtaining mean displacement errors of about 1 mm finally the method is demonstrated on data acquired from six volunteers both at rest and during exercise robustness is tested against low image quality and fast heart rates during exercise results show that our method provides a robust motion estimate in these situations

efficient algorithms for globally optimal trajectories we present serial and parallel algorithms for solving a system of equations that arises from the discretization of the hamilton jacobi equation associated to a trajectory optimization problem of the following type a vehicle starts at a prespecified point x sub o and follows a unit speed trajectory x t inside a region in spl rscr sup m until an unspecified time t that the region is exited a trajectory minimizing a cost function of the form spl int sub 0 sup t r x t dt q x t is sought the discretized hamilton jacobi equation corresponding to this problem is usually solved using iterative methods nevertheless assuming that the function r is positive we are able to exploit the problem structure and develop one pass algorithms for the discretized problem the first algorithm resembles dijkstra s shortest path algorithm and runs in time o n log n where n is the number of grid points the second algorithm uses a somewhat different discretization and borrows some ideas from a variation of dial s shortest path algorithm 1969 that we develop here it runs in time o n which is the best possible under some fairly mild assumptions finally we show that the latter algorithm can be efficiently parallelized for two dimensional problems and with p processors its running time becomes o n p provided that p o spl radic n log n

the laplacian in regions with many small obstacles fluctuations around the limit operator we consider the laplacian m in 3 or in a bounded region of 3 with dirichlet boundary conditions on the surfaces of some identical small neighborhoods ofm randomly distributed points in the limit whenm goes to infinity and their linear size decreases as 1 m we give here a stronger form of the result showing the convergence of the above operator to c x wherec x is the limit density of electrostatic capacity of the obstacles in particular results on the rate of convergence and on the fluctuations of m around the limit operator are given

a computational approach to edge detection this paper describes a computational approach to edge detection the success of the approach depends on the definition of a comprehensive set of goals for the computation of edge points these goals must be precise enough to delimit the desired behavior of the detector while making minimal assumptions about the form of the solution we define detection and localization criteria for a class of edges and present mathematical forms for these criteria as functionals on the operator impulse response a third criterion is then added to ensure that the detector has only one response to a single edge we use the criteria in numerical optimization to derive detectors for several common image features including step edges on specializing the analysis to step edges we find that there is a natural uncertainty principle between detection and localization performance which are the two main goals with this principle we derive a single operator shape which is optimal at any scale the optimal detector has a simple approximate implementation in which edges are marked at maxima in gradient magnitude of a gaussian smoothed image we extend this simple detector using operators of several widths to cope with different signal to noise ratios in the image we present a general method called feature synthesis for the fine to coarse integration of information from operators at different scales finally we show that step edge detector performance improves considerably as the operator point spread function is extended along the edge

large eddy simulation inflow conditions for coupling with reynolds averaged flow solvers hybrid approaches using a combination of reynolds averaged navier stokes rans approaches and large eddy simulations les have become increasingly popular one way to construct a hybrid approach is to apply separate flow solvers to components of a complex system and to exchange information at the interfaces of the domains for the les flow solver boundary conditions then have to be defined on the basis of the reynolds averaged flow statistics delivered by a rans flow solver this is a challenge which also arises for instance when defining les inflow conditions from experimental data the problem for the coupled rans les computations is further complicated by the fact that the mean flow statistics at the interface may vary in time and are not known a priori but only from the rans solution the present study defines a method to provide les inflow conditions through auxiliary a priori les computations where an les inflow database is generated the database is modified to account for the unsteadiness of the interface flow statistics

3 d carotid multi region mri segmentation by globally optimal evolution of coupled surfaces in this paper we propose a novel global optimization based 3 d multi region segmentation algorithm for t1 weighted black blood carotid magnetic resonance mr images the proposed algorithm partitions a 3 d carotid mr image into three regions wall lumen and background the algorithm performs such partitioning by simultaneously evolving two coupled 3 d surfaces of carotid artery adventitia boundary ab and lumen intima boundary lib while preserving their anatomical inter surface consistency such that the lib is always located within the ab in particular we show that the proposed algorithm results in a fully time implicit scheme that propagates the two linearly ordered surfaces of the ab and lib to their globally optimal positions during each discrete time frame by convex relaxation in this regard we introduce the continuous max flow model and prove its duality equivalence to the convex relaxed optimization problem with respect to each evolution step we then propose a fully parallelized continuous max flow based algorithm which can be readily implemented on a gpu to achieve high computational efficiency extensive experiments with four users using 12 3t mr and 26 1 5t mr images demonstrate that the proposed algorithm yields high accuracy and low operator variability in computing vessel wall volume in addition we show the algorithm outperforms previous methods in terms of high computational efficiency and robustness with fewer user interactions

support vector shape a classifier based shape representation we introduce a novel implicit representation for 2d and 3d shapes based on support vector machine svm theory each shape is represented by an analytic decision function obtained by training svm with a radial basis function rbf kernel so that the interior shape points are given higher values this empowers support vector shape svs with multifold advantages first the representation uses a sparse subset of feature points determined by the support vectors which significantly improves the discriminative power against noise fragmentation and other artifacts that often come with the data second the use of the rbf kernel provides scale rotation and translation invariant features and allows any shape to be represented accurately regardless of its complexity finally the decision function can be used to select reliable feature points these features are described using gradients computed from highly consistent decision functions instead from conventional edges our experiments demonstrate promising results

efficient numerical schemes for gradient vector flow since its publication more than 10 years ago the gradient vector flow gvf technique has been used and adapted to various models and problems its effectiveness has greatly contributed to its popularity the main drawback of gvf and its generalisation however is their expensive computation load and its consequence on the capture range in this work we propose and compare different efficient numerical schemes to solve the gvf and its generalisations

zonal detached eddy simulation of the flow around a high lift configuration a zonal hybrid reynolds averaged navier stokes large eddy simulation rans les approach called zonal des used to handle a two dimensional high lift configuration with deployed slat and flap is presented this method allows to reduce significantly the cost of an accurate numerical prediction of the unsteady flow around wings compared to a complete les some issues concerning grid generation as well as the use of zonal detached eddy simulation for a multi element airfoil are discussed the basic planar grid has 250 000 points and the finest spanwise grid has 31 points with az c 0 002 the effort is geared toward detailed comparison of the numerical results with the europiv2 experimental particle image velocimetry data including both mean and fluctuating properties of the velocity field arnott and al

hybrid simulation approach for cavity flows blending algorithm and boundary treatment issues the maturation of high performance computer architectures and computational algorithms has prompted the development of a new generation of models that attempt to combine the robustness and efficiency offered by the reynolds averaged navier stokes equations with the higher level of modeling offered by the equations developed for large eddy simulation the application of a new hybrid approach is discussed where the transition between these equation sets is controlled by a blending function that depends on local turbulent flow properties as well as the local mesh spacing the utilization of local turbulence properties provides added control in specifying the regions of the flow intended for each equation set removing much of the burden from the grid generation process moreover the model framework allows for the combination of existing closure model equations avoiding the difficulty of formulating a single set of closure coefficients that perform well in both reynolds averaged and large eddy simulation modes simple modifications to common second order accurate reynolds averaged navier stokes algorithms are proposed to enhance the capturing of large eddy motions

dynamic programming for detecting tracking and matching deformable contours the problem of segmenting an image into separate regions and tracking them over time is one of the most significant problems in vision terzopoulos et al 1987 proposed an approach to detect the contour regions of complex shapes assuming a user selected initial contour not very far from the desired solution we propose to further explore the information provided by the user s selected points and apply an optimal method to detect contours which allows a segmentation of the image the method is based on dynamic programming dp and applies to a wide variety of shapes it is exact and not iterative we also consider a multiscale approach capable of speeding up the algorithm by a factor of 20 although at the expense of losing the guaranteed optimality characteristic the problem of tracking and matching these contours is addressed for tracking the final contour obtained at one frame is sampled and used as initial points for the next frame then the same dp process is applied for matching a novel strategy is proposed where the solution is a smooth displacement field in which unmatched regions are allowed while cross vectors are not the algorithm is again based on dp and the optimal solution is guaranteed we have demonstrated the algorithms on natural objects in a large spectrum of applications including interactive segmentation and automatic tracking of the regions of interest in medical images

a variational level set approach to multiphase motion a coupled level set method for the motion of multiple junctions of e g solid liquid and grain boundaries which follows the gradient flow for an energy functional consisting of surface tension proportional to length and bulk energies proportional to area is developed the approach combines the level set method of s osher and j a sethian with a theoretical variational formulation of the motion by f reitich and h m soner the resulting method uses as many level set functions as there are regions and the energy functional is evaluated entirely in terms of level set functions the gradient projection method leads to a coupled system of perturbed by curvature terms hamilton jacobi equations the coupling is enforced using a single lagrange multiplier associated with a constraint which essentially prevents a regions from overlapping and b the development of a vacuum the numerical implementation is relatively simple and the results agree with and go beyond the theory as given in 12 other applications of this methodology including the decomposition of a domain into subregions with minimal interface length are discussed finally some new techniques and results in level set methodology are presented

a variational approach for the segmentation of the left ventricle in cardiac image analysis in this paper we propose a level set method to segment mr cardiac images our approach is based on a coupled propagation of two cardiac contours and integrates visual information with anatomical constraints the visual information is expressed through a gradient vector flow based boundary component and a region term that aims at best separating the cardiac contours regions according to their global intensity properties in order to deal with misleading visual support an anatomical constraint is considered that couples the propagation of the cardiac contours according to their relative distance the resulting motion equations are implemented using a level set approach and a fast and stable numerical approximation scheme the additive operator splitting encouraging experimental results are provided using real data

homogenization of the navier stokes equations with a slip boundary condition this paper deals with the homogenization of the stokes or navier stokes equations in a domain containing periodically distributed obstacles with a slip boundary condition i e the normal component of the velocity is equal to zero while the tangential velocity is proportional to the tangential component of the normal stress we generalize our previous results see 1 established in the case of a dirichlet boundary condition in particular for a so called critical size of the obstacles equal to e3 in the three dimensional case e being the inter hole distance we prove the convergence of the homogenization process to a brinkman type law

gradient vector flow fast geometric active contours in this paper we propose an edge driven bidirectional geometric flow for boundary extraction to this end we combine the geodesic active contour flow and the gradient vector flow external force for snakes the resulting motion equation is considered within a level set formulation can deal with topological changes and important shape deformations an efficient numerical schema is used for the flow implementation that exhibits robust behavior and has fast convergence rate promising results on real and synthetic images demonstrate the potentials of the flow

boundary finding with parametrically deformable models segmentation using boundary finding is enhanced both by considering the boundary as a whole and by using model based global shape information the authors apply flexible constraints in the form of a probabilistic deformable model to the problem of segmenting natural 2 d objects whose diversity and irregularity of shape make them poorly represented in terms of fixed features or form the parametric model is based on the elliptic fourier decomposition of the boundary probability distributions on the parameters of the representation bias the model to a particular overall shape while allowing for deformations boundary finding is formulated as an optimization problem using a maximum a posteriori objective function results of the method applied to real and synthetic images are presented including an evaluation of the dependence of the method on prior information and image quality