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Bridging the gap between mathematical and biological models and clinical applications could be considered as one of the new challenges of medical image analysis over the ten last years. This paper presents an advanced and convivial algorithm for brain glioblastomas tumor growth modelization. The brain glioblastomas tumor region would be extracted using a(More)
This study investigates a fast distribution-matching, data-driven algorithm for 3D multimodal MRI brain glioma tumor and edema segmentation in different modalities. We learn non-parametric model distributions which characterize the normal regions in the current data. Then, we state our segmentation problems as the optimization of several cost functions of(More)
In this paper, we are interested to segment brain MR perfusion image using active contours or deformable models in order to assist in diagnosis. Traditional methods are often unable to perform adequately on these types of images which have poor contrast, high-level speckle noise and boundary gaps. For this purpose, we propose a Modified Fuzzy C Means method(More)
Manual analysis of brain glioma tumor lacks accuracy and is time consuming. Thus, to avoid human error, this paper presents an automatic and accurate computer aided diagnosis (CAD) system for brain glioma exploration on magnetic resonance imaging. A preprocessing approach was proposed to eliminate extra-cerebral features. Tumor and even its edema was(More)
Accurate magnetic resonance brain tissue segmentation is of much importance in medical imaging. Hence segmentation methods are in research focus and various methods are presented in the literature. In this paper, a multi region graph cut image segmentation in a kernel-induced space is used for brain-tissue-segmentation framework. The RBF kernel function(More)
This research concerned one advanced methodology for automatic localization of brain tumors that could be imaged by Magnetic Resonance Image (MRI) modality. Such methodology could be based on Iterative closest point (ICP) matching technique by using axial MRI symmetry. The idea behind this work is to compare right and left hemispheres mirrored across a(More)
Multiple sclerosis is a chronic inflammatory disease of the central nervous system. Lesions detected by Magnetic resonance (MR) sequences not only confirme the diagnosis of MS, but let monitor them to determine the evolutionary state of the disease and to evaluate the therapeutic efficiency. Thus, the change in lesion load is a criterion determining the(More)
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