A.-K. Seghouane

Australian National University, Canberra, Australian Capital Territory, Australia

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Publications (36)24.03 Total impact

  • A. Shah, A.-K. Seghouane
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    ABSTRACT: Extracting region-specific hemodynamic response function (HRF) from overlapping ROIs of activated areas in the brain in noisy functional Magnetic Resonance Imaging (fMRI) data is essential when analyzing the temporal dynamics of a brain region response and its neuronal coupling for functional and effective connectivity. Based on the assumption of spatially sparse brain hemodynamics, HRFs from jointly activated overlapping regions are separated based on sparse dictionary learning. The proposed HRF estimation procedure is tested on both simulated and real fMRI data. The results reveal the efficiency of the proposed method in separating temporally-dependent inter-region HRFs from fMRI data.
    Biomedical Imaging (ISBI), 2013 IEEE 10th International Symposium on; 01/2013
  • A.-K. Seghouane, A. Shah
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    ABSTRACT: Non-parametric hemodynamic response function (HRF) estimation in noisy functional Magnetic Resonance Imaging (fMRI) plays an important role when investigating the temporal dynamic of a brain region response during activations. Assuming the drift Lipschitz continuous; a new algorithm for non-parametric HRF estimation is derived in this paper. The proposed algorithm estimates the HRF by applying a first order differencing to the fMRI time series samples. It is shown that the proposed HRF estimator is √(N) consistent. Its performance is assessed using both simulated and a real fMRI data sets obtained from an event-related fMRI experiment. The application results reveal that the proposed HRF estimation method is efficient both computationally and in term of accuracy.
    Biomedical Imaging (ISBI), 2013 IEEE 10th International Symposium on; 01/2013
  • M.U. Khalid, A.-K. Seghouane
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    ABSTRACT: In this paper a novel framework that combines data-driven methods is proposed for functional connectivity analysis of functional magnetic resonance imaging (fMRI) data. The basic idea is to overcome the shortcomings of compressed sensing based data-driven method by incorporating canonical correlation analysis (CCA) to extract a more meaningful temporal profile that is based solely on underlying brain hemodynamics, which can be further investigated to detect functional connectivity using regression analysis. We apply our method on synthetic and task-related fMRI data to show that the combined framework which better adapts to individual variations of distinct activity patterns in the brain is an effective approach to reveal functionally connected brain regions.
    Biomedical Imaging (ISBI), 2013 IEEE 10th International Symposium on; 01/2013
  • A.-K. Seghouane
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    ABSTRACT: When neural activity increases in a region of the brain, the local magnetic resonance signal produced in that part of the brain increases by a small amount owing to changes in blood oxygenation. This blood oxygenation level dependent (BOLD) effect is the basis for most functional magnetic resonance imaging (fMRI) studies done today to map patterns of activation in the working human brain. In this tutorial we will review the main techniques used to analyze fMRI data.
    Systems, Signal Processing and their Applications (WoSSPA), 2013 8th International Workshop on; 01/2013
  • A. Shah, A.-K. Seghouane
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    ABSTRACT: Discriminating between active and non-active brain voxels in noisy functional magnetic resonance imaging (fMRI) data plays an important role when investigating task-related activations of the neuronal sites. A novel method for efficiently capturing drifts in the functional magnetic resonance imaging (fMRI) data is presented that leads to enhanced fMRI activation detection. The proposed algorithm apply a first order differencing to the fMRI time series samples in order to remove the drift effect. Using linear least-squares, a consistent hemodynamic response function (HRF) of the fMRI voxel is estimated as a first-step that leads to an optimal estimate of the drift based on a wavelet thresholding technique. The de-drifted fMRI voxel response is then obtained by removing the estimated drift from the fMRI time-series. Its performance is assessed using a visual task real fMRI data set. The application results reveal that the proposed method, which avoids the selection of a model to remove the drift component, leads to an improved activation detection performance in fMRI data.
    Machine Learning for Signal Processing (MLSP), 2013 IEEE International Workshop on; 01/2013
  • A.-K. Seghouane, M. Hanif
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    ABSTRACT: A new algorithm for wavelet-based blind image restoration is presented in this paper. It is obtained by defining an intermediate variable to characterize the original image. Both the original image and the additive noise are modeled by multivariate Gaussian process. The blurring process is specified by its point spread function, which is unknown. The original image and the blur are estimated by alternating minimization of the KullbackLeibler divergence between a model family of probability distributions defined using a linear image model and a desired family of probability distributions constrained to be concentrated on the observed data. The intermediate variable is used to introduce regularization in the algorithm. The algorithm presents the advantage to provide closed form expressions for the parameters to be updated and to converge only after few iterations. A simulation example that illustrates the effectiveness of the proposed algorithm is presented.
    Machine Learning for Signal Processing (MLSP), 2012 IEEE International Workshop on; 01/2012
  • A.-K. Seghouane
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    ABSTRACT: A fundamental question in functional MRI (fMRI) data analysis is to declare pixels either activated or non-activated with respect to the experimental design. A new statistical test for detecting activated pixels in fMRI data is proposed. The test is based on comparing the dimension of the parametric models fitted to the voxels fMRI time series data with and without controlled activation-baseline pattern. A corrected variant of the Akaike information criterion, is used for this comparison. This test has the advantage of not requiring any user-specified threshold to be estimated. The effectiveness of the proposed fMRI activation detection method is illustrated on real experimental data.
    Statistical Signal Processing Workshop (SSP), 2012 IEEE; 01/2012
  • A.-K. Seghouane, M.U. Khalid
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    ABSTRACT: Brain networks explore the dependence relationships between brain regions under consideration through the estimation of the precision matrix. An approach based on linear regression is adopted here for estimating the partial correlation matrix from functional brain imaging data. Knowing that brain networks are sparse and hierarchical, the l1-norm penalized regression has been used to estimate sparse brain networks. Although capable of including the sparsity information, the l1-norm penalty alone doesn't incorporate the hierarchical structure prior information when estimating brain networks. In this paper, a new l1 regularization method that applies the sparsity constraint at hierarchical levels is proposed and its implementation described. This hierarchical sparsity approach has the advantage of generating brain networks that are sparse at all levels of the hierarchy. The performance of the proposed approach in comparison to other existing methods is illustrated on real fMRI data.
    Machine Learning for Signal Processing (MLSP), 2012 IEEE International Workshop on; 01/2012
  • A.-K. Seghouane
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    ABSTRACT: Estimation of the expected Kullback-Leibler information is the basis for deriving the Akaike information criterion (AIC) and its corrected version AIC c . Both criteria were designed for selecting multivariate regression models with an appropriateness of AIC c for small sample cases. In the work presented here, two new small sample AIC corrections are derived for multivariate regression model selection. The proposed AIC corrections are based on asymptotic approximation of bootstrap-type estimates of Kullback-Leibler information. These new corrections are of particular interest when the use of bootstrap is not really justified in terms of the required calculations. As it is the case for AIC c , the new proposed criteria are asymptotically equivalent to AIC. Simulation results demonstrate that in small sample size settings, one of the proposed criterion provides better model choices than other available model selection criteria. As a result, this proposed criterion serves as an effective tool for selecting a model of appropriate order. Asymptotic justifications for the proposed criteria are provided in the Appendix.
    IEEE Transactions on Aerospace and Electronic Systems 05/2011; · 1.30 Impact Factor
  • A.-K. Seghouane, Ju Lynn Ong
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    ABSTRACT: Computed tomographic colonography (CTC) is a promising alternative to traditional invasive colonoscopic methods used in the detection and removal of cancerous growths, or polyps in the colon. Existing algorithms for CTC typically use a classifier to discriminate between true and false positives generated by a polyp candidate detection system. However, these classifiers often suffer from a phenomenon termed the curse of dimensionality, whereby there is a marked degradation in the performance of a classifier as the number of features used in the classifier is increased. In addition an increase in the number of features used also contributes to an increase in computational complexity and demands on storage space. This paper demonstrates the benefits of feature selection with the aim at increasing specificity while preserving sensitivity in a polyp detection system. It also compares the performances of an individual (F-score) and mutual information (MI) method for feature selection on a polyp candidate database, in order to select a subset of features for optimum CAD performance. Experimental results show that the performance of SVM+MI seems to be better for a small number of features used, but the SVM+Fscore method seems to dominate when using the 30-50 best ranked features. On the whole, the AUC measures are able to reach 0.8-0.85 for the top ranked 20-40 features using MI or F-score methods compared with 0.65-0.7 when using all 100 features in the worst-case scenario.
    Image Processing (ICIP), 2010 17th IEEE International Conference on; 10/2010
  • A.-K. Seghouane
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    ABSTRACT: A new algorithm for Maximum likelihood blind image restoration is presented in this paper. It is obtained by modeling the original image and the additive noise as multivariate Gaussian processes with unknown covariance matrices. The blurring process is specified by its point spread function, which is also unknown. Estimations of the original image and the blur are derived by alternating minimization of the Kullback-Leibler divergence. The algorithm presents the advantage to provide closed form expressions for the parameters to be updated and to converge only after few iterations. A simulation example that illustrates the effectiveness of the proposed algorithm is presented.
    Image Processing (ICIP), 2010 17th IEEE International Conference on; 10/2010
  • A.-K. Seghouane, Ju Lynn Ong
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    ABSTRACT: Existing polyp detection methods rely heavily on curvature-based characteristics to differentiate between lesions. However, as curvature is a local feature and a second order differential quantity, simply inspecting the curvature at a point is not sufficient. In this paper, we propose to inspect a local neighbourhood around a candidate point using curvature maps. This candidate point is pre-identified using the geodesic centroid of a surface patch containing vertices with positive point curvature values corresponding to convex shaped protrusions. Geodesic rings are then constructed around this candidate point and point curvatures around these rings are accumulated to produce curvature maps. From this, a cumulative shape property, S for a given neighbourhood radius can be computed and used for identifying bulbous polyps which typically have a high S value, and its corresponding 'neck' region. We show that a threshold value of S > 0.48 is sufficient to discriminate between polyps and non polyps with 100% sensitivity and specificity for bulbous polyps > 10mm.
    Image Processing (ICIP), 2010 17th IEEE International Conference on; 10/2010
  • A. Seghouane, J.L. Ong
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    ABSTRACT: A fundamental question in functional MRI (fMRI) data analysis is to declare pixels either activated or non-activated with respect to the experimental design. A new statistical test for detecting activated pixels in fMRI data is proposed. The test is based on comparing the dimension of the parametric models fitted to the voxels fMRI time series data with and without controlled activation-baseline pattern. The Bayesian information criterion, is used for this comparison. This test has the advantage of not requiring any user-specified threshold to be estimated. The effectiveness of the proposed fMRI activation detection method is illustrated on real experimental data.
    Image Processing (ICIP), 2010 17th IEEE International Conference on; 10/2010
  • Ju Lynn Ong, A.-K. Seghouane
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    ABSTRACT: Existing polyp detection methods rely heavily on curvature-based characteristics to differentiate between lesions. However, as curvature is a local feature and a second order differential quantity, noise caused by small bumpy structures and incoherent curvature fields of a discretized volume or surface can greatly increase the number of false positives (FPs) detected. This paper investigates a spectral compression and curvature tensor smoothing algorithm with the aim to reduce the number of FPs detected while preserving true positives. Simulation results give 96% sensitivity for polyps >10 mm while reducing FPs by 92%.
    Biomedical Imaging: From Nano to Macro, 2009. ISBI '09. IEEE International Symposium on; 08/2009
  • A.-K. Seghouane, J.L. Ong
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    ABSTRACT: The description of lesion shapes with curvature-based features is a widespread approach in polyp detection methods. These methods are motivated by the need to compactly and accurately encode the different existing shape forms on the colon wall. However, the colon wall presents a number of small convex shape that resemble polyps which increases drastically the number of false positive (FP) detected. In this paper a method based on multiresolution shape processing using the spherical wavelet transform is proposed. The method aims to reduce the number of FPs detected while preserving true positives. Simulation results illustrating the effectiveness of the method are presented.
    Statistical Signal Processing, 2009. SSP '09. IEEE/SP 15th Workshop on; 01/2009
  • A.-K. Seghouane
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    ABSTRACT: Alternating minimization of the information divergence is used to derive an effective algorithm for maximum likelihood (ML) factor analysis. The proposed algorithm is derived as an iterative alternating projections procedure on a model family of probability distributions defined on the factor analysis model and a desired family of probability distributions constrained to be concentrated on the observed data. The algorithm presents the advantage of being simple to implement and stable to converge. A simulation example that illustrates the effectiveness of the proposed algorithm for ML factor analysis is presented.
    Machine Learning for Signal Processing, 2008. MLSP 2008. IEEE Workshop on; 11/2008
  • A.-K. Seghouane
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    ABSTRACT: The Akaike information criterion, AIC, and its corrected version, AIC<sub>c</sub> are two methods for selecting normal linear regression models. Both criteria were designed as estimators of the expected Kullback-Leibler information between the model generating the data and the approximating candidate model. In this paper, a new corrected variants of AIC is derived for the purpose of small sample linear regression model selection. The new proposed variant of AIC is based on asymptotic approximation of bootstrap type estimates of Kullback-Leibler information. Simulation results which illustrate better performance of the proposed AIC correction when applied to polynomial regression in comparison to AIC, AIC<sub>c</sub> and other criteria are presented. Asymptotic justifications for the proposed criterion are provided in the Appendix.
    Machine Learning for Signal Processing, 2008. MLSP 2008. IEEE Workshop on; 11/2008
  • Source
    Ju Lynn Ong, A.-K. Seghouane, K. Osborn
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    ABSTRACT: As an alternative procedure to the current methods which consider only the mean values of shape features to globally characterize a candidate shape polyps, probability density functions (PDFs) of some feature variables constructed based on Gaussian and mean curvatures are used to characterize the global shape of a candidate lesion. The decision on whether or not this candidate lesion is a polyp is made by comparing the density functions of the considered shape feature variables to reference PDFs of the same variables obtained from a pre- constructed polyp/non polyp data base. The Kullback-Leibler divergence is used as a dissimilarity measure to compare these PDFs and make a decision based on closeness. Experiments carried out on real data are used to illustrate the effectiveness of the proposed method in comparison to existing ones.
    Biomedical Imaging: From Nano to Macro, 2008. ISBI 2008. 5th IEEE International Symposium on; 06/2008
  • Source
    M. Kleinsteuber, A.-K. Seghouane
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    ABSTRACT: The Cramer-Rao bound (CRB) plays an important role in direction of arrival (DOA) estimation because it is always used as a benchmark for comparison of the different proposed estimation algorithms. In this correspondence, using well-known techniques of global analysis and differential geometry, four necessary conditions for the maximum of the log-likelihood function are derived, two of which seem to be new. The CRB is derived for the general class of sensor arrays composed of multiple arbitrary widely separated subarrays in a concise way via a coordinate free form of the Fisher Information. The result derived in [1] is confirmed.
    IEEE Transactions on Signal Processing 03/2008; · 2.81 Impact Factor
  • A.-K. Seghouane
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    ABSTRACT: Image restoration necessitates the choice of a regularization parameter that controls the trade-off between fidelity to the blurred noisy observed image and the smoothness of the restored image. The choice of this parameter for which several estimators have been proposed is crucial for the quality of the restored image. In this letter, two estimators for choosing the regularization parameter are proposed. One is a simple closed-form approximation to the minimum of the selection criterion, and the other is an approximation to the minimum of a mean squared error (MSE)-based criterion.
    IEEE Signal Processing Letters 02/2008; · 1.67 Impact Factor

Publication Stats

153 Citations
24.03 Total Impact Points

Institutions

  • 2006–2012
    • Australian National University
      • College of Engineering & Computer Science
      Canberra, Australian Capital Territory, Australia
  • 2005–2008
    • National ICT Australia Ltd
      Sydney, New South Wales, Australia
  • 2004–2005
    • National Institute for Research in Computer Science and Control
      Le Chesney, Île-de-France, France