International Conference Papers

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Author: search.filters.author.Lassouaoui, NadiaHas files: Yes

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    Detection and Description the Lesions in Brain Images
    (University Cadi Ayyad (Marroc), 2005-11) Lassouaoui, Nadia; Hamami, Latifa; Nouali-Taboudjemat, Nadia; Hadjar, Samir; Saadi, Hocine
    In this paper, we present the various stages for lesion recognition in brain images. We firstly apply a filtering based on geodesic reconstruction operator for increasing the quality of image. After, we use an unsupervised segmentation genetic algorithm for detecting the abnormal zones with respect of theirs morphological characteristics because they define the nature of illness (cyst, tumour, malignant, benign, …). The obtained segmented images are analyzed for computing the characteristics of illness which are necessary for the recognition stage for deducing a decision about the type of illness. So, we give also the various algorithms used for computing the morphological characteristics of lesions (size, shape, position, texture, …). Since we obtain a decision about the malignity or benignity of the lesion and a quantitative information for helping the doctors to locate the sick part.
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    Morphological geodesic reconstruction in the extraction of the lesions in brain images
    (Université Aboubekr Belkaïd –Tlemcen, 2003-09) Lassouaoui, Nadia; Hamami, Latifa
    In this paper, we present algorithms that permit to extract the lesions in brain images. The goal is to extract the abnormal zones with respect of theirs morphological haracteristics as: size, shape and position. Then, we present to the doctor a simple image of abnormal zone that is easy to analysis and correct to diagnosis. For it, we used an algorithm of edge detection based on morphological gradient for delimit this areas; moreover, we have used algorithm based on complex operator knowing by morphological geodesic reconstruction, it permits to extract the areas where the lesions exist. These algorithms are based on mathematical morphology operators. The results obtained are satisfactory; the edge detection respects the characteristics and the morphology of the sick part of the brain, whereas with the algorithm of segmentation in homogeneous areas, we could make the extraction of the sick part of the brain, with the respect of all characteristics in a reasonable time allowing to think of real-time operation.
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    Genetic algorithms and multifractal segmentation of cervical cell images
    (IEEE, 2003-07) Lassouaoui, Nadia; Hamami, Latifa
    This paper deals with the segmentation problem of cervical cell images. Knowing that the malignity criteria appear on the morphology of the core and the cytoplasm of each cell, then, the goal of this segmentation is to separate each cell on its component, that permits to analyze separately their morphology (size and shape) in the recognition step, for deducing decision about the malignity of each cell. For that, we use a multifractal algorithm based on the computation of the singularity exponent on each point of the image. For increasing the quality of the segmentation, we propose to add an optimization step based on genetic algorithms. The proposed processing has been tested on several images. Herein, we present some results obtained by two cervical cell images.