Automatic Artery/Vein Classification in 2D-DSA Images of Stroke Patients

To develop an objective system for perfusion assessment in digital subtraction angiography (DSA), artery-vein (A/V) classification is essential. In this study, an automated A/V classification system in 2D DSA images of stroke patients is proposed. After preprocessing through vessel segmentation with a Frangi fitler and Gaussian smoothing, a time-intensity curve (TIC) of each vessel pixel was extracted and relevant parameters were calculated. Different combinations of input parameters were systematically tested to come to the optimal set of input parameters. The parameters formed the input for k-means (KM) and fuzzy c-means (FCM) clustering. Both algorithms were tested for clustering into 2 to 7 clusters. Cluster labeling was performed based on the average time to peak (TTP) of a cluster. A reference standard consisted of manually annotated DSA images of the MR CLEAN registry. Outcome measures were accuracy, true artery rate (TAR) and true vein rate (TVR). The optimal value for k was found to be 2 for both KM and FCM clustering. The optimal parameter set was: variance, standard deviation, maximal slope, peak width, time to peak, arrival time, maximal intensity and area under the TIC. No significant difference was found between FCM and KM clustering and. Both FCM and KM clustering yielded an average accuracy of 76%, average TAR of 74% and average TVR of 80%