In this thesis, we present our work on the analysis of human brain MRI, in particular brain MRI of multiple sclerosis (MS) patients. The automatic extraction of quantifiers for MS has many potential applications, as well in the clinical field as for clinical trials. Initially, we devoted ourselves to the general study of the disease, in order to precise the role of the MRI in the diagnosis process.We present then the various pretreatments necessary to the robustness of the system, then the segmentation of healthy tissues via a probabilistic model of partial volume effect, as well as a new version of the EM algorithm with a specific treatment of outliers. The use of the various sequences of the acquisition protocol makes it possible to speci...
Common speech calls for patient autonomy. Yet chronic illness care also reveals attachments. This is...
The stochastic classical models include linear interactions copulas, expressing in general pair inte...
Murine models are commonly used in neuroscience to improve our knowledge of disease processes and to...
In medicine, physicians (general practitioner or specialist) realize a diagnosis to determine patien...
The electroencephalography measures the brain activity by recording variations of the electric field...
Multiple sclerosis (MS) is a demyelinating disease which affects specially the centralnervous system...
Multiple sclerosis is a chronic inflammatory and demyelinating disease of the central nervous system...
The Internet has become an important source of medical information for patients and their family mem...
This thesis presents a complete work relating to methodology developed for the neurophysiologic and ...
In this thesis we propose, implement, and evaluate algorithms improving spatial resolution in recons...
The aim of the project is to investigate the modeling of the reliability/incertitude/imprecision of ...
Estrogens are neurosteroids, especially Estradiol (17β-E2) which is considered to be the most biolog...
This thesis is a contribution to the problem of a complex system prognosis. More precisely, it conce...
Magnetic resonance imaging (MRI) allows news approaches to improve diagnostic and therapeutic issues...
Back pain is one of the first causes of surgical intervention in the world and instrumentation is ne...
Common speech calls for patient autonomy. Yet chronic illness care also reveals attachments. This is...
The stochastic classical models include linear interactions copulas, expressing in general pair inte...
Murine models are commonly used in neuroscience to improve our knowledge of disease processes and to...
In medicine, physicians (general practitioner or specialist) realize a diagnosis to determine patien...
The electroencephalography measures the brain activity by recording variations of the electric field...
Multiple sclerosis (MS) is a demyelinating disease which affects specially the centralnervous system...
Multiple sclerosis is a chronic inflammatory and demyelinating disease of the central nervous system...
The Internet has become an important source of medical information for patients and their family mem...
This thesis presents a complete work relating to methodology developed for the neurophysiologic and ...
In this thesis we propose, implement, and evaluate algorithms improving spatial resolution in recons...
The aim of the project is to investigate the modeling of the reliability/incertitude/imprecision of ...
Estrogens are neurosteroids, especially Estradiol (17β-E2) which is considered to be the most biolog...
This thesis is a contribution to the problem of a complex system prognosis. More precisely, it conce...
Magnetic resonance imaging (MRI) allows news approaches to improve diagnostic and therapeutic issues...
Back pain is one of the first causes of surgical intervention in the world and instrumentation is ne...
Common speech calls for patient autonomy. Yet chronic illness care also reveals attachments. This is...
The stochastic classical models include linear interactions copulas, expressing in general pair inte...
Murine models are commonly used in neuroscience to improve our knowledge of disease processes and to...