Noise reduction on mammographic phantom images

A noise reduction scheme on digitized mammographic phantom images is presented. This algorithm is based on a direct contrast modification method using an optimal function which is obtained by means of the mean squared error as a criterion. Computer simulated images containing objects similar to those observed in the phantom are built to evaluate the performance of the algorithm. Noise reduction results obtained on both simulated and real phantom images show that the developed method may be considered as a good pre-processing step from the point of view of automating phantom film evaluation by means of image processing

Multilevel Feature Representation of FDG-PET Brain Images for Diagnosing Alzheimer's Disease

International audienc

Multispectral texture characterization: application to computer aided diagnosis on prostatic tissue images

International audienceVarious approaches have been proposed in the literature for texture characterization of images. Some of them are based on statistical properties, others on fractal measures and some more on multi-resolution analysis. Basically, these approaches have been applied on mono-band images. However, most of them have been extended by including the additional information between spectral bands to deal with multi-band texture images. In this article, we investigate the problem of texture characterization for multi-band images. Therefore, we aim to add spectral information to classical texture analysis methods that only treat gray-level spatial variations. To achieve this goal, we propose a spatial and spectral gray level dependence method (SSGLDM) in order to extend the concept of gray level co-occurrence matrix (GLCM) by assuming the presence of texture joint information between spectral bands. Thus, we propose new multi-dimensional functions for estimating the second-order joint conditional probability density of spectral vectors. Theses functions can be represented in structure form which can help us to compute the occurrences while keeping the corresponding components of spectral vectors. In addition, new texture features measurements related to (SSGLDM) which define the multi-spectral image properties are proposed. Extensive experiments have been carried out on 624 textured multi-spectral images for use in prostate cancer diagnosis and quantitative results showed the efficiency of this method compared to the GLCM. The results indicate a significant improvement in terms of global accuracy rate. Thus, the proposed approach can provide clinically useful information for discriminating pathological tissue from healthy tissue

Automatic Segmentation and Measurement of Vasculature in Retinal Fundus Images Using Probabilistic Formulation

The automatic analysis of retinal blood vessels plays an important role in the computer-aided diagnosis. In this paper, we introduce a probabilistic tracking-based method for automatic vessel segmentation in retinal images. We take into account vessel edge detection on the whole retinal image and handle different vessel structures. During the tracking process, a Bayesian method with maximum a posteriori (MAP) as criterion is used to detect vessel edge points. Experimental evaluations of the tracking algorithm are performed on real retinal images from three publicly available databases: STARE (Hoover et al., 2000), DRIVE (Staal et al., 2004), and REVIEW (Al-Diri et al., 2008 and 2009). We got high accuracy in vessel segmentation, width measurements, and vessel structure identification. The sensitivity and specificity on STARE are 0.7248 and 0.9666, respectively. On DRIVE, the sensitivity is 0.6522 and the specificity is up to 0.9710

Alzheimer’s Disease Computer-Aided Diagnosis on Positron Emission Tomography Brain Images Using Image Processing Techniques

Positron emission tomography (PET) is a molecular medical imaging modality which is commonly used for neurodegenerative disease diagnosis. Computer-aided diagnosis (CAD), based on medical image analysis, could help with the quantitative evaluation of brain diseases such as Alzheimer’s disease (AD). Ranking the effectiveness of brain volume of interest (VOI) to separate healthy or normal control (HC or NC) from AD brain PET images is presented in this book chapter. Brain images are first mapped into anatomical VOIs using an atlas. Different features including statistical, graph, or connectivity-based features are then computed on these VOIs. Top-ranked VOIs are then input into a support vector machine (SVM) classifier. The developed methods are evaluated on a local database image as well as on Alzheimer’s Disease Neuroimaging Initiative (ADNI) public database and then compared to known selection feature methods. These new approaches outperformed classification results in the case of a two-group separation

Improved control strategy of DFIG-based wind turbines using direct torque and direct power control techniques

This paper presents different control strategies for a variable-speed wind energy conversion system (WECS), based on a doubly fed induction generator. Direct Torque Control (DTC) with Space-Vector Modulation is used on the rotor side converter. This control method is known to reduce the fluctuations of the torque and flux at low speeds in contrast to the classical DTC, where the frequency of switching is uncontrollable. The reference for torque is obtained from the maximum power point tracking technique of the wind turbine. For the grid-side converter, a fuzzy direct power control is proposed for the control of the instantaneous active and reactive power. Simulation results of the WECS are presented to compare the performance of the proposed and classical control approaches.Peer reviewedFinal Accepted Versio

Traitement du signal tensoriel. Application à l'imagerie hyperspectrale

Cette thèse est consacrée au développement et à l étude de méthodes algébriques pour l analyse des données hyperspectrales. Une nouvelle représentation des données par un tenseur d ordre 3 a permis la proposition de méthodes originales, impliquant l utilisation d outils d algèbre multilinéaire. De ce fait, les méthodes développées sont dites multidimensionnelles ou multimodales. Basées sur la décomposition tensorielle de TUCKER, elles analysent conjointement le mode spatial et spectral. Cette thèse répond à deux problématiques. La première concerne la réduction du bruit. Une technique de détection robuste au bruit est proposée en incorporant un filtrage de Wiener multimodal. Les filtres de Wiener n-modaux (spatiaux et spectraux) sont estimés en minimisant l erreur quadratique moyenne entre le tenseur utile et estimé. La deuxième problématique abordée est la réduction de la dimension spectrale. Le fléau de la grande dimension des données dégrade les estimations statistiques lors du processus de classification des données. Dans ce cadre, nous avons développé une méthode basée sur la réduction du mode spectral par transformation linéaire, qui approxime simultanément le mode spatial en rangs inférieurs. Les deux méthodes multimodales sont respectivement évaluées en observant leur impact sur la qualité de détection et de classification. Ces résultats révèlent l intérêt de considérer une analyse spatiale/spectrale par rapport aux techniques uniquement spectrales ou hybrides analysant séquentiellement le mode spectral et spatial.AIX-MARSEILLE3-BU Sc.St Jérô (130552102) / SudocSudocFranceF