Volumetric segmentation of magnetic resonance images

Current computer graphics techniques can generate 3-D views of the human anatomy from magnetic resonance images. These techniques require that the images first be segmented into the various tissue types. However, there has been no fully automated system that can perform this task on a single set of high-resolution 3-D magnetic resonance images. We present a fully automated segmentation algorithm based on the 3-D difference of Gaussians (DOG) filter. A novel method for the classification of regions found by the DOG filter, as well as a correction procedure that detects errors from the DOG filter, is presented. Regions are classified based on the mean gray level of the voxels within closed contours. In previous work, the user had to manually split falsely merged regions. Our automated correction algorithm detects such errors and splits the merged regions. Spatial information is also incorporated to help discriminate between tissues. Encouraging results were obtained with an average of less than five percent error in each image. Integral shading is used to obtain a 3-D rendering of the data set.