Mahmoud Mostapha

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We propose a new MAP-based technique for the unsuper-vised segmentation of different brain structures (white matter, gray matter , etc.) from T1-weighted MR brain images. In this paper, we follow a procedure like most conventional approaches, in which T1-weighted MR brain images and desired maps of regions (white matter, gray matter , etc.) are modeled by a(More)
In this paper, we propose a novel framework for the automated extraction of the brain from T1-weighted MR images. The proposed approach is primarily based on the integration of a stochastic model [a two-level Markov-Gibbs random field (MGRF)] that serves to learn the visual appearance of the brain texture, and a geometric model (the brain isosurfaces) that(More)
Magnetic resonance imaging (MRI) modalities have emerged as powerful means that facilitate non-invasive clinical diagnostics of various diseases and abnormalities since their inception in the 1980s. Multiple MRI modalities, such as different types of the sMRI and DTI, have been employed to investigate facets of ASD in order to better understand this complex(More)
In this paper, we propose a new adaptive atlas-based technique for the automated segmentation of brain tissues (white matter and grey matter) from infant diffusion tensor images (DTI). Brain images and desired region maps (brain, Cerebrospinal fluid, etc.) are modeled by a joint Markov-Gibbs random field (MGRF) model of independent image signals and(More)
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