Automatic Identification of Grey Matter Structures from MRI to Improve the Segmentation of White Matter Lesions

The segmentation of MRI scans of patients with white matter lesions (WML) is difficult because the MRI characteristics of white matter lesions are similar to those of grey matter. Intensity based statistical classification techniques misclassify some WML as grey matter and some grey matter as WML. We developed a fast elastic matching algorithm that warps a reference data set containing information about the location of the grey matter into the approximate shape of the patient’s brain. The region of white matter was segmented after segmenting the cortex and deep grey matter structures. The cortex was identified using a 3D region growing algorithm constrained by anatomical, intensity gradient and tissue class parameters. White matter and white matter lesions were then segmented without interference from grey matter using a two class minimum distance classifier. Analysis of double echo spin echo MRI scans of sixteen patients with clinically determined multiple sclerosis (MS) was carried out. The segmentation of the cortex and deep grey matter structures provides anatomical context. This was found to improve the segmentation of MS lesions by allowing correct classification of the white matter region despite the overlapping tissue class distributions of grey matter and MS lesion.