Results 1  10
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42
A Bayesian model for joint segmentation and registration
 NEUROIMAGE
, 2006
"... A statistical model is presented that combines the registration of an atlas with the segmentation of magnetic resonance images. We use an Expectation Maximizationbased algorithm to find a solution within the model, which simultaneously estimates image artifacts, anatomical labelmaps, and a structur ..."
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Cited by 47 (2 self)
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A statistical model is presented that combines the registration of an atlas with the segmentation of magnetic resonance images. We use an Expectation Maximizationbased algorithm to find a solution within the model, which simultaneously estimates image artifacts, anatomical labelmaps, and a structuredependent hierarchical mapping from the atlas to the image space. The algorithm produces segmentations for brain tissues as well as their substructures. We demonstrate the approach on a set of 22 magnetic resonance images. On this set of images, the new approach performs significantly better than similar methods which sequentially apply registration and segmentation.
Spatially coherent clustering using graph cuts
 In CVPR (2
, 2004
"... Feature space clustering is a popular approach to image segmentation, in which a feature vector of local properties (such as intensity, texture or motion) is computed at each pixel. The feature space is then clustered, and each pixel is labeled with the cluster that contains its feature vector. A ma ..."
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Cited by 42 (0 self)
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Feature space clustering is a popular approach to image segmentation, in which a feature vector of local properties (such as intensity, texture or motion) is computed at each pixel. The feature space is then clustered, and each pixel is labeled with the cluster that contains its feature vector. A major limitation of this approach is that feature space clusters generally lack spatial coherence (i.e., they do not correspond to a compact grouping of pixels). In this paper, we propose a segmentation algorithm that operates simultaneously in feature space and in image space. We define an energy function over both a set of clusters and a labeling of pixels with clusters. In our framework, a pixel is labeled with a single cluster (rather than, for example, a distribution
Multiphase Dynamic Labeling for Variational RecognitionDriven Image Segmentation
 In ECCV 2004, LNCS 3024
, 2004
"... We propose a variational framework for the integration multiple competing shape priors into level set based segmentation schemes. ..."
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Cited by 31 (9 self)
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We propose a variational framework for the integration multiple competing shape priors into level set based segmentation schemes.
Bayesian Hyperspectral Image Segmentation with Discriminative Class Learning
"... Abstract. This paper presents a new Bayesian approach to hyperspectral image segmentation that boosts the performance of the discriminative classifiers. This is achieved by combining class densities based on discriminative classifiers with a MultiLevel Logistic MarkovGibs prior. This density favor ..."
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Cited by 17 (9 self)
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Abstract. This paper presents a new Bayesian approach to hyperspectral image segmentation that boosts the performance of the discriminative classifiers. This is achieved by combining class densities based on discriminative classifiers with a MultiLevel Logistic MarkovGibs prior. This density favors neighbouring labels of the same class. The adopted discriminative classifier is the Fast Sparse Multinomial Regression. The discrete optimization problem one is led to is solved efficiently via graph cut tools. The effectiveness of the proposed method is evaluated, with simulated and real AVIRIS images, in two directions: 1) to improve the classification performance and 2) to decrease the size of the training sets. 1
T.: Segmentation of High Angular Resolution Diffusion MRI Modeled as a Field of von MisesFisher Mixtures
 In: European Conference on Computer Vision (ECCV). Volume 3953
, 2006
"... Abstract. High angular resolution diffusion imaging (HARDI) permits the computation of water molecule displacement probabilities over a sphere of possible displacement directions. This probability is often referred to as the orientation distribution function (ODF). In this paper we present a novel m ..."
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Cited by 15 (0 self)
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Abstract. High angular resolution diffusion imaging (HARDI) permits the computation of water molecule displacement probabilities over a sphere of possible displacement directions. This probability is often referred to as the orientation distribution function (ODF). In this paper we present a novel model for the diffusion ODF namely, a mixture of von MisesFisher (vMF) distributions. Our model is compact in that it requires very few variables to model complicated ODF geometries which occur specifically in the presence of heterogeneous nerve fiber orientation. We also present a Riemannian geometric framework for computing intrinsic distances, in closedform, and performing interpolation between ODFs represented by vMF mixtures. As an example, we apply the intrinsic distance within a hidden Markov measure field segmentation scheme. We present results of this segmentation for HARDI images of rat spinal cords – which show distinct regions within both the white and gray matter. It should be noted that such a fine level of parcellation of the gray and white matter cannot be obtained either from contrast MRI scans or Diffusion Tensor MRI scans. We validate the segmentation algorithm by applying it to synthetic data sets where the ground truth is known. 2 1
Edge preserving spatially varying mixtures for image segmentation
"... A new hierarchical Bayesian model is proposed for image segmentation based on Gaussian mixture models (GMM) with a prior enforcing spatial smoothness. According to this prior, the local differences of the contextual mixing proportions (i.e. the probabilities of class labels) are Student’s tdistribu ..."
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Cited by 11 (1 self)
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A new hierarchical Bayesian model is proposed for image segmentation based on Gaussian mixture models (GMM) with a prior enforcing spatial smoothness. According to this prior, the local differences of the contextual mixing proportions (i.e. the probabilities of class labels) are Student’s tdistributed. The generative properties of the Student’s tpdf allow this prior to impose smoothness and simultaneously model the edges between the segments of the image. A maximum a posteriori (MAP) expectationmaximization (EM) based algorithm is used for Bayesian inference. An important feature of this algorithm is that all the parameters are automatically estimated from the data in closed form. Numerical experiments are presented that demonstrate the superiority of the proposed model for image segmentation as compared to standard GMMbased approaches and to GMM segmentation techniques with ”standard” spatial smoothness constraints. 1.
Entropy controlled gaussmarkov random measure field models for early vision
 In LNCS 3752, SpringerVerlag VLSM 2005
, 2005
"... We present a computationally efficient segmentationrestoration method, based on a probabilistic formulation, for the joint estimation of the label map (segmentation) and the parameters of the feature generator models (restoration). Our algorithm computes an estimation of the posterior marginal proba ..."
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Cited by 9 (5 self)
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We present a computationally efficient segmentationrestoration method, based on a probabilistic formulation, for the joint estimation of the label map (segmentation) and the parameters of the feature generator models (restoration). Our algorithm computes an estimation of the posterior marginal probability distributions of the label field based on a Gauss Markov Random Measure Field model. Our proposal introduces a explicit entropy control for the estimated posterior marginals, therefore improving the parameter estimation step. If the model parameters are given, our algorithm computes the posterior marginals as the global minimizer of a quadratic, linearly constrained energy function; therefore, one can compute very efficiently the optimal (Maximizer of the Posterior Marginals or MPM) estimator for multi–class segmentation problems. Moreover, a good estimation of the posterior marginals allows one to compute estimators different from the MPM for restoration problems, denoising and optical flow computation. Experiments demonstrate better performance over other state of the art segmentation approaches. 1.
A spatially constrained generative model and an EM algorithm for image segmentation
 IEEE Transactions on Neural Networks
"... Abstract—In this paper, we present a novel spatially constrained generative model and an expectation–maximization (EM) algorithm for modelbased image segmentation. The generative model assumes that the unobserved class labels of neighboring pixels in the image are generated by prior distributions w ..."
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Cited by 8 (1 self)
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Abstract—In this paper, we present a novel spatially constrained generative model and an expectation–maximization (EM) algorithm for modelbased image segmentation. The generative model assumes that the unobserved class labels of neighboring pixels in the image are generated by prior distributions with similar parameters, where similarity is defined by entropic quantities relating to the neighboring priors. In order to estimate model parameters from observations, we derive a spatially constrained EM algorithm that iteratively maximizes a lower bound on the data loglikelihood, where the penalty term is datadependent. Our algorithm is very easy to implement and is similar to the standard EM algorithm for Gaussian mixtures with the main difference that the labels posteriors are “smoothed ” over pixels between each Eand Mstep by a standard image filter. Experiments on synthetic and real images show that our algorithm achieves competitive segmentation results compared to other Markovbased methods, and is in general faster. Index Terms—Bound optimization, expectation–maximization (EM) algorithm, hidden Markov random fields (MRFs), image
Bayesian image segmentation using waveletbased priors
 Proc. IEEE Conf. Computer Vision and Pattern Recognition  CVPR’2005
, 2005
"... This paper introduces a formulation which allows using waveletbased priors for image segmentation. This formulation can be used in supervised, unsupervised, or semisupervised modes, and with any probabilistic observation model (intensity, multispectral, texture). Our main goal is to exploit the wel ..."
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Cited by 6 (2 self)
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This paper introduces a formulation which allows using waveletbased priors for image segmentation. This formulation can be used in supervised, unsupervised, or semisupervised modes, and with any probabilistic observation model (intensity, multispectral, texture). Our main goal is to exploit the wellknown ability of waveletbased priors to model piecewise smoothness (which underlies stateoftheart methods for denoising, coding, and restoration) and the availability of fast algorithms for waveletbased processing. The main obstacle to using waveletbased priors for segmentation is that they’re aimed at representing real values, rather than discrete labels, as needed for segmentation. This difficulty is sidestepped by the introduction of realvalued hidden fields, to which the labels are probabilistically related. These hidden fields, being unconstrained and realvalued, can be given any type of spatial prior, such as one based on wavelets. Under this model, Bayesian MAP segmentation is carried out by a (generalized) EM algorithm. Experiments on synthetic and real data testify for the adequacy of the approach. 1.