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)
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)
This paper presents a novel approach for extracting the brain from 3D T1-weighted MR images. The proposed approach combines a stochastic two-level Markov-Gibbs random field (MGRF) image model with a geometric model that parcels the brain into a set of nested iso-surfaces using a fast marching level setmethod. The classification of each brain voxel found on(More)
This paper introduces a new adaptive atlas-based framework for the automated segmentation of different brain structures from infant diffusion tensor images (DTI). To model the brain images and their desired region maps, we used a joint Markov-Gibbs random field (MGRF) model that accounts for three image descriptors: (i) a 1<sup>st</sup>-order visual(More)
Segmentation is a key task in medical image analysis because its accuracy significantly affects successive steps. Automatic segmentation methods often produce inadequate segmentations, which require the user to manually edit the produced segmentation slice by slice. Because editing is time-consuming, an editing tool that enables the user to produce accurate(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)
This paper introduces a novel adaptive atlas-based framework for the automated segmentation of different brain structures from infant magnetic resonance (MR) brain images. The proposed framework provides a more accurate segmentation of different infant brain structures in the isointense age stage (6-12 months) by integrating diffusion tensor imaging (DTI)(More)