A new Bayesian model for image segmentation based on a Gaussian mixture model is proposed. The model structure allows the automatic determination of the number of segments while ensuring spatial smoothness of the final output. This is achieved by defining two separate mixture weight sets: the first set of weights is spatially variant and incorporates an MRF edge-preserving smoothing prior; the second set of weights is governed by a Dirichlet prior in order to prune unnecessary mixture components. The model is trained using variational inference and the Majorization-Minimization (MM) algorithm, resulting in closed-form parameter updates. The algorithm was successfully evaluated in terms of various segmentation indices using the Berkeley image data base.

In medical imaging, constructing an atlas and bringing an image set in a single common reference frame may easily lead the analysis to erroneous conclusions, especially when the population under study is heterogeneous. In this paper, we propose a framework based on spectral clustering that is capable of partitioning an image population into sets that require a separate atlas, and identifying the most suitable templates to be used as coordinate reference frames. The spectral analysis step relies on pairwise distances that express anatomical differences between subjects as a function of the diffeomorphic warp required to match the one subject onto the other, plus residual information. The methodology is validated numerically on artificial and medical imaging data.

Two speech feature sets, RMS rhythmicity and formant frequencies F1-F4, are analyzed for their ability to distinguish alcoholized from sober speech. We describe the statistical framework based on the Alcohol Language Corpus (ALC), including other factors such as gender, age and speaking style, and its application to our case. Rhythm features are calculated using a new method based solely on the short-time energy function; formant features are derived using the standard formant tracker SNACK. Our findings indicate that 3 rhythm and 3 formant features have a high potential to detect intoxication within the speech data of a subject. We also tested the hypothesis that vowels are more centralized in the F1/F2 space for alcoholized speech, but found that, on the contrary, subjects tend to hyperarticulate when being tested for intoxication.

We propose a new approach for the restoration of polarimetric Stokes images, capable of simultaneously segmenting and restoring the images. In order to easily handle the admissibility constraints inherent to Stokes images, a proper transformation of the images is introduced. This transformation exploits the correspondence between any Stokes vector and the covariance matrix of the two components of the electric vector of the light wave. A Bayesian model based on a mixture of Gaussian kernels is used for the transformed images. Inference is achieved using the EM framework. To quantify the performances of this approach, the algorithm is tested with both synthetic and real data. We note that the pixels of the restored Stokes images issued from our approach are always physically admissible which is not the case for the nai¿ve pseudo-inverse approach.

This correspondence addresses the inversion of 3-D transformation fields, which is a problem that typically arises in image warping problems. A topology preserving parametric B-spline-based representation of the deformation field is considered. Topology preservation ensures that the transformation is a one-to-one mapping and consequently that it is invertible. Inverting such transformation fields amounts to solving a system of nonlinear equations. To tackle this problem, we rely on interval analysis techniques. The proposed algorithm yields a solution whose accuracy is user-controlled. This method may be extended to any dense transformation field and also to deformations defined on a grid of points, by considering a projection in the space of topology preserving B-spline-based deformation fields. The performance of the algorithm is illustrated on transformation fields coming from intersubject brain registration.

This communication deals with data reduction and regression. A set of high dimensional data (e.g., images) usually has only a few degrees of freedom with corresponding variables that are used to parameterize the original data set. Data understanding, visualization and classification are the usual goals. The proposed method reduces data considering a unique set of low-dimensional variables and a user-defined cost function in the multidimensional scaling framework. Mapping of the reduced variables to the original data is also addressed, which is another contribution of this work. Typical data reduction methods, such as Isomap or LLE, do not deal with this important aspect of manifold learning. We also tackle the inversion of the mapping, which makes it possible to project high-dimensional noisy points onto the manifold, like PCA with linear models. We present an application of our approach to several standard data sets such as the SwissRoll.

This article proposes a new approach to the deconvolution of electron energy loss spectra, used for material characterization. This approach is based on local filters with varying bandwidth, adapted to the local (non-stationary) characteristics of the signal to restore. The local filter synthesis is achieved by means of three parameters determined by optimizing a compound criterion. The effectiveness of this approach is shown on an example

The goal of this letter is to study convergence conditions for a previously presented iterative wavelet denoising method and to shed light on its relationship with outlier rejection. This method involves a user-defined parameter, which must fulfill certain conditions in order to ensure denoising. Using generalized Gaussian modeling for the wavelet coefficients distribution, we obtain a lower bound for this parameter, and the resulting threshold, both adapted to the shape of the distribution. The properties of this threshold are examined, and the proposed method is compared with other classical rejection methods.

This paper deals with topology preservation in three-dimensional (3-D) deformable image registration. This work is a nontrivial extension of, which addresses the case of two-dimensional (2-D) topology preserving mappings. In both cases, the deformation map is modeled as a hierarchical displacement field, decomposed on a multiresolution B-spline basis. Topology preservation is enforced by controlling the Jacobian of the transformation. Finding the optimal displacement parameters amounts to solving a constrained optimization problem: The residual energy between the target image and the deformed source image is minimized under constraints on the Jacobian. Unlike the 2-D case, in which simple linear constraints are derived, the 3-D B-spline-based deformable mapping yields a difficult (until now, unsolved) optimization problem. In this paper, we tackle the problem by resorting to interval analysis optimization techniques. Care is taken to keep the computational burden as low as possible. Results on multipatient 3-D MRI registration illustrate the ability of the method to preserve topology on the continuous image domain.

The goal of this paper is to shed new light on a wavelet-based denoising method developed by Hadjileontiadis et al. (1997, 2000) which is derived from an iterative denoising algorithm by Coifman and Wickerhauser (1995, 1998). The underlying algorithm is revisited and interpreted as a fixed-point algorithm. This allows us to derive a new version of the algorithm largely increasing computational efficiency.

The subject of this communication is the restoration of spiky sequences distorted by a linear system and corrupted by an additive noise. A (now) classical way of coping with this problem is to use a Bayesian approach with a BernoulliGaussian (bg) prior model of the sequence. We will refine this method using a Bernoulli-Gaussian plus Gaussian (bgg) prior model. This estimation method requires maximization of a posterior probability distribution, which cannot be performed optimally. Thus we propose here a new non-Bayesian estimation scheme, derived from the Kullback-Leibler information or cross-entropy. This quite general method, called the Maximum Entropy on the Mean Method (memm) in [1] and [2], is firmly based on convex analysis and yields a unique solution which can be efficiently calculated in practice, and which is, in this sense, truly optimal. As a conclusion, we present results obtained with both methods on a synthetic case. 1. PROBLEM STATEMENT In this communication, we add...