Browsing by Author "Hadjadj, Zineb"
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- ItemA cooperative framework for automated segmentation of tumors in brain MRI images(Springer, 2023-03) Hadjadj, ZinebBrain tumor segmentation from 2D Magnetic Resonance Images (MRI) is an important task for several applications in the field of medical analysis. Commonly, this task is performed manually by medical professionals, but it is not always obvious due to similarities between tumors and normal tissue and variations in tumor appearance. Therefore, the automation of medical image segmentation remains a real challenge that has attracted the attention of several researchers in recent years. Instead of choosing between region and contour approaches, in this article, we propose a region-edge cooperative method for brain tumor segmentation from MRI images. The region approach used is support vector machines (SVMs), one of the popular and highly motivated classification methods, the method distinguishes between normal and abnormal pixels based on some features: intensity and texture. To control and guide the segmentation region, we take advantage of the Ron Kimmel geodesic Active Contour Model (ACM) which produces a good delimitation of the boundaries of the object. The two methods have been cooperated sequentially in order to obtain a flexible and effective system for brain tumor segmentation. Experimental studies are performed on synthetic and real 2D MRI images of various modalities from the radiology unit of the university hospital center in Bab El Oued Algeria. The used MRI images represent various tumor shapes, locations, sizes, and intensities. The proposed cooperative framework outperformed SVM-based segmentation and ACM-based segmentation when executed independently.
- ItemA tracking approach for text line segmentation in handwritten documents(Springer, 2017-02-24) Setitra, Insaf; Hadjadj, Zineb; Meziane, AbdelkrimTracking of objects in videos consists of giving a label to the same object moving in different frames. This labeling is performed by predicting position of the object given its set of features observed in previous frames. In this work, we apply the same rationale by considering each connected component in the manuscript as a moving object and to track it so that to minimize the distance and angle of the connected component to its nearest neighbor. The approach was applied to images of ICDAR 2013 handwritten segmentation contest and proved to be robust against text orientation, size and writing script.
- ItemA tracking approach for text line segmentation in handwritten documents(CERIST, 2017-02-24) Setitra, Insaf; Hadjadj, Zineb; Meziane, AbdelkrimTracking of objects in videos consists of giving a label to the same object moving in different frames. This labeling is performed by predicting position of the object given its set of features observed in previous frames. In this work, we apply the same rationale by considering each connected component in the manuscript as a moving object and to track it so that to minimize the distance and angle of the connected component to its nearest neighbor. The approach was applied to images of ICDAR 2013 handwritten segmentation contest and proved to be robust against text orientation, size and writing script.
- ItemBinarization of Degraded Historical Document Images(CERIST, 2014) Hadjadj, Zineb; Meziane, Abdelkrim; Cheriet, Mohamed; Cherfa, YazidOld document images often suffer from different types of degradation that render their binarization a challenging task. In this paper, a new binarization algorithm for degraded document images is presented. The method is based on active contours evolving according to intrinsic geometric measures of the document image; Niblack’s thresholding is also used to control the active contours propagation. The validity of the proposed method is demonstrated on both recent and historical document images including different types of degradations, the results are compared with a number of known techniques in the literature
- ItemBinarization of document images with various object sizes(IEEE, 2017-04) Hadjadj, Zineb; Meziane, AbdelkrimThere are a lot of document image binarization techniques that try to differentiate between foreground and background but many of them fail to correctly detect all the text pixels because of degradations. In this paper, a new binarization method for document images is presented. The proposed method is based on the most commonly used binarization method: Sauvola’s, which performs relatively well on classical documents, however, three main defects remain: the window parameter of Sauvola’s formula does not fit automatically to the image content, is not robust to low contrasts, and not invariant with respect to contrast inversion. Thus for some documents, the content may not be retrieved correctly. In this paper we try to overcome one of the limitations of Sauvola’s binarization which is the Handling badly various object sizes. The well-known Chan-Vese active contour model is use in combination with the computed Sauvola’s binarization step to guarantee good quality binarization for both small and large objects inside a single document, without adjusting manually the window size to the document content. The efficiency of the proposed method is shown on several document images with various object sizes.
- ItemBinarization of Document Images with Various Object Sizes(CERIST, 2017-02-01) Hadjadj, Zineb; Meziane, AbdelkrimThere are a lot of document image binarization techniques that try to differentiate between foreground and background but many of them fail to correctly detect all the text pixels because of degradations. In this paper, a new binarization method for document images is presented. The proposed method is based on the most commonly used binarization method: Sauvola’s, which performs relatively well on classical documents, however, three main defects remain: the window parameter of Sauvola’s formula does not fit automatically to the image content, is not robust to low contrasts, and not invariant with respect to contrast inversion. Thus for some documents, the content may not be retrieved correctly. In this paper we try to overcome one of the limitations of Sauvola’s binarization which is the Handling badly various object sizes. The well-known Chan-Vese active contour model is use in combination with the computed Sauvola’s binarization step to guarantee good quality binarization for both small and large objects inside a single document, without adjusting manually the window size to the document content. The efficiency of the proposed method is shown on several document images with various object sizes.
- ItemEfficient Machine Learning-based Approach for Brain Tumor Detection Using the CAD System(Taylor & Francis, 2023-04) Guerroudji, Mohamed Amine; Hadjadj, Zineb; Lichouri, Mohamed; Amara, Kahina; Zenati, NadiaMedical research has focused on improving diagnosis through medical imaging in recent decades. Computer Assisted Diagnosis (CAD) systems have been developed to help doctors identify suspicious areas of interest, particularly those with cancer-like characteristics. CAD systems employ various algorithms and techniques to extract important numerical measurements from medical images that clinicians can use to evaluate patient conditions. This study proposes a statistical classification-based approach to efficient brain cancer detection. The proposed approach operates in three stages: first, Gradient Vector Flow (GVF) Snake models and mathematical morphology techniques retrieve regions of interest. The second stage characterizes these regions using morphological and textural parameters. Finally, a Bayesian network uses this description as input to identify malignant and benign cancer classes. We also compared the performance of the Bayesian network with other popular classification algorithms, including SVM, MLP, KNN, Random Forest, Decision Tree, XGBoost, LGBM, Gaussian Process, and RBF SVM. The results showed the superiority of the Bayesian network for the task of brain tumor classification. The proposed approach has been experimentally validated, with a sensitivity of 100% and a classification accuracy of over 98% for tumors, demonstrating the high efficiency of cancer cell segmentation.
- ItemISauvola: Improved Sauvola’s Algorithm for Document Image Binarization(CERIST, 2015-10-12) Hadjadj, Zineb; Meziane, Abdelkrim; Cherfa, Yazid; Cheriet, MohamedBinarization of historical documents is difficult and is still an open area of research. In this paper, a new binarization technique for document images is presented. The proposed technique is based on the most commonly used binarization method: Sauvola's, which performs relatively well on classical documents, however, three main defects remain: the window parameter of Sauvola's formula does not fit automatically to the image content, is not robust to low contrasts, and not invariant with respect to contrast inversion. Thus on documents such as magazines, the content may not be retrieved correctly. In this paper we use the image contrast that is defined by the local image minimum and maximum in combination with the computed Sauvola’s binarization step to guarantee good quality binarization for both low and correctly contrasted objects inside a single document, without adjusting manually the user-defined parameters to the document content. The efficiency of the proposed method is shown on both recent and historical document images of the datasets that are used in DIBCO datasets including different types of degradations.
- ItemLeap motion controller for upper limbs physical rehabilitation in post-stroke patients: a usability evaluation(2022-05) Hadjadj, Zineb; Masmoudi, Mostefa; Meziane, Abdelkrim; Zenati, NadiaStroke in Algeria is one of the most important causes of severe physical disability. Since the disease strongly influences the quality of life of patients, optimal solutions for the treatment of post-stroke patients are needed. The use of new technologies in the field of rehabilitation aims to reduce the impact of functional problems. Recent studies have shown that technologies such as virtual reality and video games can provide a way that can motivate and help patients recover their motor skills. In this paper, our objective is to evaluate the usability of the Leap Motion Controller virtual reality system (LMC), which is a sensor that captures the movement of the patient's hands and fingers without the need to place sensors or devices on the body, with serious games specifically designed for upper limbs rehabilitation in post-stroke patients. We measured the usability of the LMC system used with serious games as well as the level of satisfaction among healthy participants and post-stroke patients from Bounaama Djilali Hospital (CHU Douera) in Algeria. The results show favorable data, for the first time, the LMC is a usable tool, measured by the System Usability Scale (SUS). In addition, participants demonstrated good motivation, enjoyment and the majority of them said that they would like to use the proposed system in future treatment. Nevertheless, further studies are needed to confirm these preliminary findings.
- ItemLow-cost haptic glove for grasp precision improvement in Virtual Reality-Based Post-Stroke Hand Rehabilitation(IEEE, ) Masmoudi, Mostefa; Zenati, Nadia; Benbelkacem , Samir; Hadjadj, ZinebStroke in Algeria is one of the most important causes of severe physical disability. Upper limb paralysis is also most common in stroke patients, which severely affecting their daily life. Therefore, it is important to help stroke patients to improve the quality of their life. In this article, we have proposed a novel system based on virtual reality for fine motor rehabilitation. Because the sense of touch is essential to the patient's daily activities, we have integrated haptic feedback into our system (vibrating glove), this is to help the patient to perform rehabilitation exercises. The proposed vibrating glove is equipped with five small and flat vibrating motor discs (one on each finger); these motors are controlled by ESP8266 board. This system has been tested on two patients with stroke. The preliminary results show that the system can help patients recover fine motor skills.