Browsing by Author "Hamami, L."

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    The box counting method for evaluate the fractal dimension in radiographic images
    (2007) Harrar, Khaled; Hamami, L.
    Since the end of the Seventies, following work of the French mathematician Mandelbrot, We are witnessing the true exploitation of the interest expressed by the scientific community for the fractals objects. The aim of this paper is to introduce the fractal theory by the calculation of the fractal dimension in the radiographic images. We implement for that, the box counting method for the segmentation of the images. This method will be presented, as well as a study of the effect of the change of the rang of the box sizes (rmin and rmax) on fractal dimension is carried out
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    Osteoporosis assessment using Multilayer Perceptron neural networks
    (IEEE, 2012) Harrar, Khaled; Hamami, L.; Akkoul, S.; Lespessailles, E.
    The objective of this paper is to investigate the effectiveness of a Multilayer Perceptron (MLP) to discriminate subjects with and without osteoporosis using a set of five parameters characterizing the quality of the bone structure. These parameters include Age, Bone mineral content (BMC), Bone mineral density (BMD), fractal Hurst exponent (Hmean) and coocurrence texture feature (CoEn). The purpose of the study is to detect the potential usefulness of the combination of different features to increase the classification rate of 2 populations composed of osteporotic patients and control subjects. k-fold Cross Validation (CV) was used in order to assess the accuracy and reliability of the neural network validation. Compared to other methods MLP-based analysis provides an accurate and reliable platform for osteoporosis prediction. Moreover, the results show that the combination of the five features provides better performance in terms of discrimination of the subjects
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    Quantification de la Porosité par Analyse des Images Osseuses pour la Détection de l'Ostéoporose
    (2010) Harrar, Khaled; Hamami, L.
    L'ostéoporose est une maladie caractérisée par la raréfaction de la masse osseuse et la détérioration de la micro-architecture du tissu osseux, qui entraînent une fragilité osseuse accrue et, par conséquent, une augmentation du risque de fracture. L'objectif de cet article est de quantifier la porosité des images radiographiques, afin de pouvoir détecter l'ostéoporose. Deux méthodes sont utilisées pour l'analyse des images osseuses, la lacunarité et le star volume. La première méthode est basée sur les fractals et la seconde sur l'évaluation de l'espace médullaire. Les résultats montrent une corrélation entre les paramètres architecturaux calculés et les taux de densités minérales osseuses (DMO), ce dernier paramètre qui est le plus utilisé en routine clinique pour la détection de l'ostéoporose
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    A wavelet optimization approach for ECG signal classification
    (2012) Daamouche, Abdelhamid; Hamami, L.; Alajlan, N.; Melgani, F.
    Wavelets have proved particularly effective for extracting discriminative features in ECG signal classification. In this paper, we show that wavelet performances in terms of classification accuracy can be pushed further by customizing them for the considered classification task. A novel approach for generating the wavelet that best represents the ECG beats in terms of discrimination capability is proposed. It makes use of the polyphase representation of the wavelet filter bank and formulates the design problem within a particle swarm optimization (PSO) framework. Experimental results conducted on the benchmark MIT/BIH arrhythmia database with the state-of-the-art support vector machine (SVM) classifier confirm the superiority in terms of classification accuracy and stability of the proposed method over standard wavelets (i.e., Daubechies and Symlet wavelets)