Dual probabilistic classifier for three-dimensional neuroimaging from MRI data

摘要

The paper addresses 3D neuroimaging from MRI data by using a dual probabilistic classifier. The goals are: to enable to see thru the scalp and skull in order to observe the cortical surface and brain deep structures, to achieve a correct appearance of gyration, and to provide tools easy to use by the medical professional. MRI head data is automatically segmented into two regions: the brain (along with some subarachnoid structures and some pare of the outer CSF filling the sulci and fissures) and the outer structures (including the scalp, skull marrow, dura mater). The brain and the outer structures are classified separately using a probabilistic classifier. A new volume is created so as to eliminate the density overlap between the brain and the outer structures. Color and opacity transfer functions suitable to render the volume are generated automatically based on the density probability plots for both regions. Preliminary results are discussed.