Publications Scientifiques

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Subject: search.filters.subject.Segmentation

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    Study and implementation of u-net encoder-decoder neural network for brain tumors segmentation
    (Springer, 2023) Cherifi, Dalila; Bekkouche, Abdelghani; Bayou, Meroua; Benmalek, Assala; Mechti, Ines; Bekkour, Belkacem; Amine, Chaima; Ahmed, Halak
    Emerging advanced technologies have seen a revolution of applications into medical field, in all its aspects and sides, this has helped healthcare practitioners and empowered them in achieving accurate diagnosis and treatment, specifically with the evolution of computer Aided Diagnosis systems which use image processing techniques, Computer vision,and deep learning applied on different medical images in order to diagnose the image, or sections of the image with particular diseases or illnesses. Medical images of multiples organs or parts of the body (Liver, brain, kidney, skin, etc..) can today be visualized thanks to the advanced medical imaging techniques that exists in the market (MRI, CT, etc…) these technologies uses high energy in order to acquire high quality images but high energy can harm human cells, this is why we us low energy and with this used we get slightly low quality medical images, and here technology intervenes where we can use preprocessing techniques in order to increase image resolution prior to perform diagnosis either by doctor or CAD system. We present in this paper a computer aided diagnosis system that provides an automated brain tissue segmentation applied on 3D MRI images with its four different modalities (T1, T1C, T2, T2 weighted) of BRatS 2020 challenge dataset, by implementing a U-Net like deep neural network which provides information about classification of brain tissue into healthy tissue, Edema, Enhancing tumour, Non enhancing tumour. The model achieved an accuracy of 99.01% and dice coefficient of 47.95% after 35 epochs of training
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    Edge detection of MRI brain images based on segmentation and classification using support vector machines and neural networks pattern recognition
    (Springer, 2023) Iourzikene, Zouhir; Benazzouz, Djamel; Gougam, Fawzi
    Brain tumor (brain cancer) is a mass of abnormal cells that grow in the brain in an uncontrolled way. Brain CT and brain MRI are the most frequently performed examinations. The objective of this paper is to develop a method for the classification of brain MRI images of healthy cases and tumor cases. MRI brain database is obtained by preprocessing, segmentation, feature extraction. Feature extraction based on support vector machines (clustering) is used in this research. The objective of this method is to create several vectors and each vector contains a number of features of each image, so that we can make the classification by these features
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    Strategy of detecting abnormal behaviors by fuzzy logic
    (IEEE, 2017) Chebi, Hocine; Acheli, Dalila; Kesraoui, Mohamed
    This work falls within the framework of the video surveillance research axis. This work falls within the scope of video surveillance. It involves a link between automatic processing and problems related to video surveillance. The job is to analyze video streams coming from a network of surveillance cameras, deployed in an area of interest in order to detect abnormal behavior. Our approach in this article relies on the new application and the use of fuzzy logic in the case of division and fusion of the crowd. The detection of these behaviors will increase the speed of response of the security services in order to perform accurate analysis and detection of events in real time
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    A stochastic multi-agent approach for medical-image segmentation: Application to tumor segmentation in brain MR images
    (ELSEVIER, 2020) Bennai, Mohamed Tahar; Guessoum, Zahia; Mazouzi, Smaine; Cormier, Stéphane; Mezghiche, Mohamed
    According to functional or anatomical modalities, medical imaging provides a visual representation of complex structures or activities in the human body. One of the most common processing methods applied to those images is segmentation, in which an image is divided into a set of regions of interest. Human anatomical complexity and medical image acquisition artifacts make segmentation of medical images very complex. Thus, several solutions have been proposed to automate image segmentation. However, most existing solutions use prior knowledge and/or require strong interaction with the user. In this paper, we propose a multi-agent approach for the segmentation of 3D medical images. This approach is based on a set of autonomous, interactive agents that use a modified region growing algorithm and cooperate to segment a 3D image. The first organization of agents allows region seed placement and region growing. In a second organization, agent interaction and collaboration allow segmentation refinement by merging the over-segmented regions. Experiments are conducted on magnetic resonance images of healthy and pathological brains. The obtained results are promising and demonstrate the efficiency of our method.
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    A generic Multi-Agent framework for Medical-Image segmentation
    (2017) Bennai, Mohamed Tahar; Guessoum, Zahia; Mazouzi, Smaine; Cormier, Stéphane; Mezghiche, Mohamed
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    Intelligent detection without modeling of behavior unusual by fuzzy logic
    (Springer, 2017) Chebi, Hocine; Acheli, Dalila; Kesraoui, Mohamed
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    Towards a generic Multi-agent approach for medical image segmentation
    (Springer, 2017) Bennai, Mohamed Tahar; Guessoum, Zahia; Mazouzi, Smaine; Cormier, Stéphane; Mezghiche, Mohamed
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    Detection method without crowd behavior modeling by fuzzy logic
    (2017) Chebi, Hocine; Acheli, Dalila; Kesraoui, Mohamed
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    Multifractal analysis revisited by the continuous wavelet transform applied in lithofacies segmentation from well-logs data
    (2011) Ouadfeul, S.; Aliouane, Leila
    The main goal of this paper is to use the wavelet transform modulus maxima lines (WTMM) and the detrended fluctuations analysis (DFA) methods to establish a new technique of lithofacies segmentation from well logs data. The WTMM is used to delimitate lithoafacies boundaries and the DFA is used to provide an exact estimation of the roughness coefficient of lithofacies. Application of the proposed idea at the synthetic and real data of a borehole located in Berkine basin shows that the proposed technique can enhance reservoirs characterization