An adaptive segmentation algorithm for time-of-flight MRA data

A three-dimensional (3-D) representation of cerebral vessel morphology is e ssential for neuroradiologists treating cerebral aneurysms, However, curren t imaging techniques cannot provide such a representation, Slices of MR ang iography (MRA) data can only give two-dimensional (2-D) descriptions and am biguities of aneurysm position and size arising in X-ray projection images can often be intractable, To overcome these problems, we have established a new automatic statistically based algorithm for extracting the 3-D vessel information from time-of-flight (TOF) MRA data. We introduce distributions for the data, motivated by a physical model of blood flow, that are used in a modified version of the expectation maximization (EM) algorithm, The est imated model parameters are then used to classify statistically the voxels into vessel or other brain tissue classes, The algorithm is adaptive becaus e the model fitting is performed recursively so that classifications are ma de on local subvolumes of data, We present results from applying our algori thm to several real data sets that contain both artery and aneurysm structu res of various sizes.