ADAPTIVE SEGMENTATION OF MRI DATA

Intensity-based classification of MR images has proven problematic, ev en when advanced techniques are used, Intrascan and interscan intensit y inhomogeneities are a common source of difficulty, While reported me thods have had some success in correcting intrascan inhomogeneities, s uch methods require supervision for the individual scan, This paper de scribes a new method called adaptive segmentation that uses knowledge of tissue intensity properties and intensity inhomogeneities to correc t and segment MR images, Use of the expectation-maximization (EM) algo rithm leads to a method that allows for more accurate segmentation of tissue types as well as better visualization of magnetic resonance ima ging (MRI) data, that has proven to be effective in a study that inclu des more than 1000 brain scans, Implementation and results are describ ed for segmenting the brain in the following types of images: axial (d ual-echo spin-echo), coronal [three dimensional Fourier transform (3-D FT) gradient-echo T1-weighted] all using a conventional head coil, and a sagittal section acquired using a surface coil, The accuracy of ada ptive segmentation was found to be comparable with manual segmentation , and closer to manual segmentation than supervised multivariant class ification while segmenting gray and white matter.