multimodality image registration by maximization of mutual information a new approach to the problem of multimodality medical image registration is proposed using a basic concept from information theory mutual information mi or relative entropy as a new matching criterion the method presented in this paper applies mi to measure the statistical dependence or information redundancy between the image intensities of corresponding voxels in both images which is assumed to be maximal if the images are geometrically aligned maximization of mi is a very general and powerful criterion because no assumptions are made regarding the nature of this dependence and no limiting constraints are imposed on the image content of the modalities involved the accuracy of the mi criterion is validated for rigid body registration of computed tomography ct magnetic resonance mr and photon emission tomography pet images by comparison with the stereotactic registration solution while robustness is evaluated with respect to implementation issues such as interpolation and optimization and image content including partial overlap and image degradation our results demonstrate that subvoxel accuracy with respect to the stereotactic reference solution can be achieved completely automatically and without any prior segmentation feature extraction or other preprocessing steps which makes this method very well suited for clinical applications

stereo without epipolar lines a maximum flow formulation this paper describes a new algorithm for solving the stereo correspondence problem with a global 2 d optimization by transforming it into a maximum flow problem in a graph this transformation effectively removes explicit use of epipolar geometry thus allowing direct use of multiple cameras with arbitrary geometries the maximum flow solved both efficiently and globally yields a minimum cut that corresponds to a disparity surface for the whole image at once this global and efficient approach to stereo analysis allows the reconstruction to proceed in an arbitrary volume of space and provides a more accurate and coherent depth map than the traditional stereo algorithms in particular smoothness is applied uniformly instead of only along epipolar lines while the global optimality of the depth surface is guaranteed results show improved depth estimation as well as better handling of depth discontinuities while the worst case running time is o n 1 5 d 1 5 log nd the observed average running time is o n 1 2 d 1 3 for an image size of n pixels and depth resolution d

two stage orthogonal least squares methods for neural network construction a number of neural networks can be formulated as the linear in the parameters models training such networks can be transformed to a model selection problem where a compact model is selected from all the candidates using subset selection algorithms forward selection methods are popular fast subset selection approaches however they may only produce suboptimal models and can be trapped into a local minimum more recently a two stage fast recursive algorithm tsfra combining forward selection and backward model refinement has been proposed to improve the compactness and generalization performance of the model this paper proposes unified two stage orthogonal least squares methods instead of the fast recursive based methods in contrast to the tsfra this paper derives a new simplified relationship between the forward and the backward stages to avoid repetitive computations using the inherent orthogonal properties of the least squares methods furthermore a new term exchanging scheme for backward model refinement is introduced to reduce computational demand finally given the error reduction ratio criterion effective and efficient forward and backward subset selection procedures are proposed extensive examples are presented to demonstrate the improved model compactness constructed by the proposed technique in comparison with some popular methods

capacity of a post channel with and without feedback we consider finite state channels where the state of the channel is its previous output we refer to these as previous output is the state post channels we first focus on post channels these channels have binary inputs and outputs where the state determines if the channel behaves as a z or an s channel both with parameter we show that the nonfeedback capacity of the post channel equals its feedback capacity despite the memory of the channel the proof of this surprising result is based on showing that the induced output distribution when maximizing the directed information in the presence of feedback can also be achieved by an input distribution that does not utilize the feedback we show that this is a sufficient condition for the feedback capacity to equal the nonfeedback capacity for any finite state channel we show that the result carries over from the post channel to a binary post channel where the previous output determines whether the current channel will be binary with parameters a b or b a finally we show that in general feedback may increase the capacity of a post channel

a novel training algorithm for rbf neural network using a hybrid fuzzy clustering approach this paper introduces a novel clustering based algorithm to train gaussian type radial basis function neural networks in contrast to existing approaches we develop a specialized learning strategy that combines the merits of fuzzy and crisp clustering crisp clustering is a fast process yet very sensitive to initialization on the other hand fuzzy clustering reduces the dependency on initialization however it constitutes a slow learning process the proposed strategy aims to search for a trade off among these two potentially different effects the produced clusters possess fuzzy and crisp areas and therefore the final result is a hybrid partition where the fuzzy and crisp conditions coexist the hybrid clusters are directly involved in the estimation process of the neural network s parameters specifically the center elements of the basis functions coincide with cluster centers while the respective widths are calculated by taking into account the topology of the hybrid clusters to this end the network s design becomes a fast and efficient procedure the proposed method is successfully applied to a number of experimental cases where the produced networks prove to be highly accurate and compact in size

a hybrid learning algorithm combined with generalized rls approach for radial basis function neural networks in this paper a new hybrid learning method for radial basis function neural networks based on generalized recursive least square algorithm is proposed firstly the generalized recursive least square grls model including a general quadratic weight decay term in the energy function for the training of rbf neural networks is described then combined with the grls approach a new hybrid learning method is proposed to meet the design goals improving the generalization ability of the trained network finally experimental results demonstrate that our approach can achieve a significantly improved generalization performance of the rbf networks

a fast nonlinear model identification method the identification of nonlinear dynamic systems using linear in the parameters models is studied a fast recursive algorithm fra is proposed to select both the model structure and to estimate the model parameters unlike orthogonal least squares ols method fra solves the least squares problem recursively over the model order without requiring matrix decomposition the computational complexity of both algorithms is analyzed along with their numerical stability the new method is shown to require much less computational effort and is also numerically more stable than ols

hammer hierarchical attribute matching mechanism for elastic registration a new approach is presented for elastic registration of medical images and is applied to magnetic resonance images of the brain experimental results demonstrate very high accuracy in superposition of images from different subjects there are two major novelties in the proposed algorithm first it uses an attribute vector i e a set of geometric moment invariants gmis that are defined on each voxel in an image and are calculated from the tissue maps to reflect the underlying anatomy at different scales the attribute vector if rich enough can distinguish between different parts of an image which helps establish anatomical correspondences in the deformation procedure it also helps reduce local minima by reducing ambiguity in potential matches this is a fundamental deviation of our method referred to as the hierarchical attribute matching mechanism for elastic registration hammer from other volumetric deformation methods which are typically based on maximizing image similarity second in order to avoid being trapped by local minima i e suboptimal poor matches hammer uses a successive approximation of the energy function being optimized by lower dimensional smooth energy functions which are constructed to have significantly fewer local minima this is achieved by hierarchically selecting the driving features that have distinct attribute vectors thus drastically reducing ambiguity in finding correspondence a number of experiments demonstrate that the proposed algorithm results in accurate superposition of image data from individuals with significant anatomical differences

a constructive hybrid structure optimization methodology for radial basis probabilistic neural networks in this paper a novel heuristic structure optimization methodology for radial basis probabilistic neural networks rbpnns is proposed first a minimum volume covering hyperspheres mvch algorithm is proposed to select the initial hidden layer centers of the rbpnn and then the recursive orthogonal least square algorithm rolsa combined with the particle swarm optimization pso algorithm is adopted to further optimize the initial structure of the rbpnn the proposed algorithms are evaluated through eight benchmark classification problems and two real world application problems a plant species identification task involving 50 plant species and a palmprint recognition task experimental results show that our proposed algorithm is feasible and efficient for the structure optimization of the rbpnn the rbpnn achieves higher recognition rates and better classification efficiency than multilayer perceptron networks mlpns and radial basis function neural networks rbfnns in both tasks moreover the experimental results illustrated that the generalization performance of the optimized rbpnn in the plant species identification task was markedly better than that of the optimized rbfnn

minimizing nonsubmodular functions with graph cuts a review optimization techniques based on graph cuts have become a standard tool for many vision applications these techniques allow to minimize efficiently certain energy functions corresponding to pairwise markov random fields mrfs currently there is an accepted view within the computer vision community that graph cuts can only be used for optimizing a limited class of mrf energies e g submodular functions in this survey we review some results that show that graph cuts can be applied to a much larger class of energy functions in particular nonsubmodular functions while these results are well known in the optimization community to our knowledge they were not used in the context of computer vision and mrf optimization we demonstrate the relevance of these results to vision on the problem of binary texture restoration

communication over individual channels a communication problem in considered where no mathematical model is specified for the channel the achievable rates are determined as a function of the channel input and output sequences known a posteriori without assuming any a priori relation between them for discrete channels the empirical mutual information between the input and output sequences is shown to be achievable while for continuous channels the achievable rate is based on the empirical correlation between the sequences a rate adaptive scheme employing feedback which achieves these rates asymptotically with a guaranteed reliability without prior knowledge of the channel behavior is presented

a novel learning based dissimilarity metric for rigid and non rigid medical image registration by using bhattacharyya distances image registration is one of the major technologies of the medical image processing and analysis according to the nature of the transformation image registration can be categorized into two main classes rigid registration and non rigid registration in this paper a novel learning based dissimilarity metric and its approximation are proposed and respectively adopted to the rigid and non rigid registration of medical images this novel metric utilizes bhattacharyya distances to measure the dissimilarity of the testing image pairs by incorporating the expected intensity distributions which is learnt from the registered training image pairs in order to test the robustness and accuracy of the proposed metric in the multi modal rigid registration one thousand eight hundred randomized ctt1 registrations were performed and evaluated by the retrospective image registration evaluation rire project for the non rigid registration the approximation of the proposed dissimilarity metric is adopted with the markov random field mrf modeled non rigid registration approach non rigid registration experiments of 2d synthetic images with artificial deformation fields and 3d images from the simulated brain database were performed and evaluated respectively in this paper the experimental results of both rigid and non rigid medical image registrations demonstrated that the proposed dissimilarity metric outperforms the state of the art approach mutual information and the close related approach kullbackleibler distances highlightswe propose a novel learning based dissimilarity metric for both rigid and non rigid medical image registrations the novel dissimilarity metric is based on the bhattacharyya distance a lot of experiments were done to evaluate our method in term of accuracy and robustness we compared the proposed method with state of the art methods of both rigid and non rigid image registration our method is more robust against different challenging cases with consistently higher accuracy

fast approximate energy minimization via graph cuts many tasks in computer vision involve assigning a label such as disparity to every pixel a common constraint is that the labels should vary smoothly almost everywhere while preserving sharp discontinuities that may exist e g at object boundaries these tasks are naturally stated in terms of energy minimization the authors consider a wide class of energies with various smoothness constraints global minimization of these energy functions is np hard even in the simplest discontinuity preserving case therefore our focus is on efficient approximation algorithms we present two algorithms based on graph cuts that efficiently find a local minimum with respect to two types of large moves namely expansion moves and swap moves these moves can simultaneously change the labels of arbitrarily large sets of pixels in contrast many standard algorithms including simulated annealing use small moves where only one pixel changes its label at a time our expansion algorithm finds a labeling within a known factor of the global minimum while our swap algorithm handles more general energy functions both of these algorithms allow important cases of discontinuity preserving energies we experimentally demonstrate the effectiveness of our approach for image restoration stereo and motion on real data with ground truth we achieve 98 percent accuracy

universal communication over arbitrarily varying channels consider the problem of universally communicating over an arbitrarily varying channel i e a channel comprised of an unknown arbitrary sequence of memoryless channels it is shown that there is a communication system using feedback and common randomness that asymptotically attains with high probability the capacity of the time averaged channel universally for every sequence of channels this attainable rate is optimal under certain conditions while no prior knowledge of the channel sequence is assumed the capacity of the time averaged channel meets or exceeds the traditional arbitrarily varying channel avc capacity for every memoryless avc defined over the same alphabets and therefore the system universally attains the random code avc capacity without knowledge of the avc parameters the presented system combines rateless coding with a universal prediction scheme for the input prior distribution from which the codebook is randomly drawn because at each point in time the future of the channel sequence is unknown to the communicators the adaptation of the input behavior by universally predicting the prior plays a major role in the result

rateless codes for avc models the arbitrarily varying channel avc is a channel model whose state is selected maliciously by an adversary fixed blocklength coding assumes a worst case bound on the adversary s capabilities which leads to pessimistic results this paper defines a variable length perspective on this problem for which achievable rates are shown that depend on the realized actions of the adversary specifically rateless codes are constructed which require a limited amount of common randomness these codes are constructed for two kinds of avc models in the first the channel state cannot depend on the channel input and in the second it can as a by product the randomized coding capacity of the avc with state depending on the transmitted codeword is found and shown to be achievable with a small amount of common randomness the results for this model are proved using a randomized strategy based on list decoding

achieving marton s region for broadcast channels using polar codes this paper presents polar coding schemes for the two user discrete memoryless broadcast channel dm bc which achieve marton s region with both common and private messages this is the best achievable rate region known to date and it is tight for all classes of two user dm bcs whose capacity regions are known to accomplish this task we first construct polar codes for both the superposition as well as binning strategy by combining these two schemes we obtain marton s region with private messages only finally we show how to handle the case of common information the proposed coding schemes possess the usual advantages of polar codes i e they have low encoding and decoding complexity and a superpolynomial decay rate of the error probability we follow the lead of goela abbe and gastpar who recently introduced polar codes emulating the superposition and binning schemes to align the polar indices for both schemes their solution involves some degradedness constraints that are assumed to hold between the auxiliary random variables and channel outputs to remove these constraints we consider the transmission of k blocks and employ a chaining construction that guarantees the proper alignment of the polarized indices the techniques described in this paper are quite general and they can be adopted to many other multiterminal scenarios whenever there polar indices need to be aligned

minimax universal decoding with an erasure option motivated by applications of rateless coding decision feedback and automatic repeat request arq we study the problem of universal decoding for unknown channels in the presence of an erasure option specifically we harness the competitive minimax methodology developed in earlier studies in order to derive a universal version of forney s classical erasure list decoder which in the erasure case optimally trades off between the probability of erasure and the probability of undetected error the proposed universal erasure decoder guarantees universal achievability of a certain fraction xi of the optimum error exponents of these probabilities in a sense to be made precise in the sequel a single letter expression for xi which depends solely on the coding rate and the neyman pearson threshold to be defined is provided the example of the binary symmetric channel is studied in full detail and some conclusions are drawn

a new image thresholding method based on gaussian mixture model in this paper an efficient approach to search for the global threshold of image using gaussian mixture model is proposed firstly a gray level histogram of an image is represented as a function of the frequencies of gray level then to fit the gaussian mixtures to the histogram of image the expectation maximization em algorithm is developed to estimate the number of gaussian mixture of such histograms and their corresponding parameterization finally the optimal threshold which is the average of these gaussian mixture means is chosen and the experimental results show that the new algorithm performs better

orthogonal least squares learning algorithm for radial basis function networks the radial basis function network offers a viable alternative to the two layer neural network in many applications of signal processing a common learning algorithm for radial basis function networks is based on first choosing randomly some data points as radial basis function centers and then using singular value decomposition to solve for the weights of the network such a procedure has several drawbacks and in particular an arbitrary selection of centers is clearly unsatisfactory the authors propose an alternative learning procedure based on the orthogonal least squares method the procedure chooses radial basis function centers one by one in a rational way until an adequate network has been constructed in the algorithm each selected center maximizes the increment to the explained variance or energy of the desired output and does not suffer numerical ill conditioning problems the orthogonal least squares learning strategy provides a simple and efficient means for fitting radial basis function networks this is illustrated using examples taken from two different signal processing applications

density evolution for asymmetric memoryless channels density evolution de is one of the most powerful analytical tools for low density parity check ldpc codes and graph codes with message passing decoding algorithms with channel symmetry as one of its fundamental assumptions density evolution has been widely and successfully applied to different channels including binary erasure channels becs binary symmetric channels bscs binary additive white gaussian noise biawgn channels etc this paper generalizes density evolution for asymmetric memoryless channels which in turn broadens the applications to general memoryless channels e g z channels composite white gaussian noise channels etc the central theorem underpinning this generalization is the convergence to perfect projection for any fixed size supporting tree a new iterative formula of the same complexity is then presented and the necessary theorems for the performance concentration theorems are developed several properties of the new density evolution method are explored including stability results for general asymmetric memoryless channels simulations code optimizations and possible new applications suggested by this new density evolution method are also provided this result is also used to prove the typicality of linear ldpc codes among the coset code ensemble when the minimum check node degree is sufficiently large it is shown that the convergence to perfect projection is essential to the belief propagation bp algorithm even when only symmetric channels are considered hence the proof of the convergence to perfect projection serves also as a completion of the theory of classical density evolution for symmetric memoryless channels

the uniform distribution as a universal prior in this correspondence we discuss the properties of the uniform prior as a universal prior i e a prior that induces a mutual information that is simultaneously close to the capacity for all channels we determine bounds on the amount of the mutual information loss in using the uniform prior instead of the capacity achieving prior specifically for the class of binary input channels with any output alphabet we show that the z channel has the minimal mutual information with uniform prior out of all channels with a given capacity from this we conclude that the degradation of the mutual information with respect to the capacity is at most 0 011 bit and as was shown previously at most 6 a related result is that the capacity achieving prior for any channel is not far from uniform some of these results are extended to channels with nonbinary input

correction to an algebraic analytic and algorithmic investigation on the capacity and capacity achieving input probability distributions of finite input finite output discrete memoryless channels mar 08 1003 1023 in this paper we investigate the capacity and capacity achieving input probability distributions ipds of finite input finite output discrete memoryless channels dmcs in the general respect we establish a novel and simple characterization for the capacity achieving ipds of a dmc which is equivalent to the conventional kuhn tucker conditions we then prove a conjecture of majani and rumsey which claims that every probability component of each capacity achieving ipd of a dmc with positive capacity is less than 1 e 1 where e 2 71828182 is the base of natural logarithms it remains an open problem whether there exists an explicit closed form solution for the capacity and capacity achieving ipds of a general finite input finite output dmc except for the two input two output dmc in the algebraic respect we demonstrate that there does not in general exist an algebraic solution for the capacity achieving ipds of an m input n output dmc for any m ges 2 and any n ges 3 in the analytic respect however we can obtain an explicit closed form analytic solution represented as an infinite series for the capacity achieving ipd of a two input three output dmc we also provide a formula for the average capacity of weakly symmetric dmcs and show that the average capacity in nats per channel use of the n input m output weakly symmetric dmcs increases for n ges 2 but has a finite limit of 1 gamma as n rarr infin where gamma 0 57721566 is euler s constant in the algorithmic respect the convergence of the arimoto blahut algorithm is proved in a direct and elementary way a new and simple iterative algorithm for calculating a capacity achieving ipd is then proposed which is provably convergent for all dmcs with positive transition probabilities finally the characterization and determination of the set of all capacity achieving ipds of a dmc are addressed

mrf based deformable registration and ventilation estimation of lung ct deformable image registration is an important tool in medical image analysis in the case of lung computed tomography ct registration there are three major challenges large motion of small features sliding motions between organs and changing image contrast due to compression recently markov random field mrf based discrete optimization strategies have been proposed to overcome problems involved with continuous optimization for registration in particular its susceptibility to local minima however to date the simplifications made to obtain tractable computational complexity reduced the registration accuracy we address these challenges and preserve the potentially higher quality of discrete approaches with three novel contributions first we use an image derived minimum spanning tree as a simplified graph structure which copes well with the complex sliding motion and allows us to find the global optimum very efficiently second a stochastic sampling approach for the similarity cost between images is introduced within a symmetric diffeomorphic b spline transformation model with diffusion regularization the complexity is reduced by orders of magnitude and enables the minimization of much larger label spaces in addition to the geometric transform labels hyper labels are introduced which represent local intensity variations in this task and allow for the direct estimation of lung ventilation we validate the improvements in accuracy and performance on exhale inhale ct volume pairs using a large number of expert landmarks

non rigid image registration of brain magnetic resonance images using graph cuts we present a graph cuts based method for non rigid medical image registration on brain magnetic resonance images in this paper the non rigid medical image registration problem is reformulated as a discrete labeling problem based on a voxel to voxel intensity similarity measure each voxel in the source image is assigned a displacement label which represents a displacement vector indicating which position in the floating image it is spatially corresponding to in the proposed method a smoothness constraint based on the first derivative is used to penalize sharp changes in the adjacent displacement labels across voxels the image registration problem is therefore modeled by two energy terms based on intensity similarity and smoothness of the displacement field these energy terms are submodular and can be optimized by using the graph cuts method via email protected expansions which is a powerful combinatorial optimization tool and capable of yielding either a global minimum or a local minimum in a strong sense using the realistic brain phantoms obtained from the simulated brain database we compare the registration results of the proposed method with two state of the art medical image registration approaches free form deformation based method and demons method in addition the registration results are also compared with that of the linear programming based image registration method it is found that the proposed method is more robust against different challenging non rigid registration cases with consistently higher registration accuracy than those three methods and gives realistic recovered deformation fields

a gaussian input is not too bad we consider the problem of choosing a robust input for communicating over an input constrained additive noise channel where the noise distribution is arbitrary we show that the mutual information rate achievable using a white gaussian input never incurs a loss of more than half a bit per sample with respect to the power constrained capacity for comparison for the family of colored gaussian noise channels a white gaussian input loses at most log e 2e spl ap 0 265 bit per sample with respect to the optimum water pouring solution for general input constraints we derive a formula for choosing the best input in the min max capacity loss bound sense the bound on the capacity loss is tight for pulse position modulation ppm in the presence of a bursty jammer

determining the centers of radial basis probabilistic neural networks by recursive orthogonal least square algorithms in this paper we adopt a recursive orthogonal least squares algorithm rolsa to train radial basis probabilistic neural networks rbpnn and select the corresponding hidden centers from the training samples the rolsa is first used to recursively find the weights between the second hidden layer and the output layer of the rbpnn then the basic principle to select the hidden centers from the training set and a detailed selection procedure are presented the solution to orthogonal decomposition terms under the condition of varying hidden centers is obtained theoretically finally the effectiveness and efficiency of our proposed approach are demonstrated by two examples