A Novel 3D Segmentation Method of the Lumen from Intravascular Ultrasound Images

In this paper a novel method that automatically detects the lumenintima border on an intravascular ultrasound sequence (IVUS) is presented. First, a 3D co-occurrence matrix was used to efficiently extract the texture information of the IVUS images through the temporal sequence. By extracting several co-occurrence matrices a complete characterization feature space was determined. Secondly, using a k-means algorithm, all the pixels in the IVUS images were classified by determining if they belong to either the lumen or the other vessel tissues. This enables automatic clustering and therefore no learning step was required. The classification of the pixels within the feature space was obtained using 3 clusters: two clusters for the vessel tissues, one cluster for the lumen, while the remaining pixels are labeled as unclassified. Experimental results show that the proposed method is robust to noisy images and yields segmented lumen-intima contours validated by an expert in more than 80% of a total of 300 IVUS images.