tomographic angiography (CTA) being noninvasive, economical and informative, has become a common modality for
monitoring disease status and treatment effects. Here, we present a new method for detecting and quantifying coronary
arterial stenosis via CTA using fuzzy distance transform (FDT) approach. FDT computes local depth at each image point
in the presence of partial voluming. Coronary arterial stenoses are detected and their severities are quantified by
analyzing FDT values along the medial axis of an artery obtained by skeletonization. Also, we have developed a new
skeletal pruning algorithm toward improving quality of medial axes and therefore, enhancing the accuracy of stenosis
detection and quantification. The method is completed using the following steps - (1) fuzzy segmentation of coronary
artery via CTA, (2) FDT computation of coronary arteries, (3) medial axis computation, (4) estimation of local diameter
along arteries and (5) stenosis detection and quantification of arterial blockage. Performance of the method has been
quantitatively evaluated on a realistic coronary artery phantom dataset with randomly simulated stenoses and the results
are compared with a classical binary algorithm. The method has also been applied on a clinical CTA dataset from
thirteen patients with 59 stenoses and the results are compared with an expert's quantitative assessment of stenoses.
Results of the phantom experiment indicate that the new method is significantly more accurate as compared to the
conventional binary method. Also, the results of the clinical study indicate that the computerized method is highly in
agreement with the expert's assessments.