Ankylosing Spondylitis is a disease characterized by abnormal bone structures (syndesmophytes) growing at intervertebral disk spaces. Because this growth is so slow as to be undetectable on plain radiographs taken over years, it is desirable to resort to computerized techniques to complement qualitative human judgment with precise quantitative measures. We developed an algorithm with minimal user intervention that provides such measures using high-resolution computed tomography (CT) images. To the best of our knowledge it is the first time that determination of the disease's status is attempted by direct measurement of the syndesmophytes. The first part of our algorithm segments the whole vertebral body using a 3-D multiscale cascade of successive level sets. The second part extracts the continuous ridgeline of the vertebral body where syndesmophytes are located. For that we designed a novel level set implementation capable of evolving on the isosurface of an object represented by a triangular mesh using curvature features. The third part of the algorithm segments the syndesmophytes from the vertebral body using local cutting planes and quantitates them. We present experimental work done with 10 patients from each of which we processed five vertebrae. The results of our algorithm were validated by comparison with a semi-quantitative evaluation made by a medical expert who visually inspected the CT scans. Correlation between the two evaluations was found to be 0.936 ( p < 10(-18)) .