Fully Automated Integrated Segmentation of Carotid Artery Ultrasound Images Using DBSCAN and Affinity Propagation

Purpose B-mode ultrasound images are used in identifying the presence of fat deposit if any in carotid artery. The intima media, lumen, bifurcation boundary is detected by the echogenic characteristics embedded in the carotid artery. Methods A fully automatic self-learning based segmentation is proposed by extracting the edges by a modified affinity propagation, which are given as inputs to the Density Based Spatial Clustering of Applications with Noise (DBSCAN) for super pixel segmentation. The segmented results are analyzed with Gradient Vector Flow (GVF) snake model and Particle Swarm Optimization (PSO) clustering based segmentation using various performance measures. Results The proposed parameter free, fully automatic segmentation method combining Affinity propagation and DBSCAN are evaluated for a database of 361 images and gives reinforced results in the longitudinal B-mode ultrasound images. The proposed approach gives an improved accuracy of 12% increase when compared with the manual segmentation and 15% compared with segmentation by affinity propagation and DBSCAN when performed individually. The average Root Mean Square Error (RMSE) is 110 +/- 44 mu m. Conclusion Extracted edge points are used for clustering in a fully automated carotid artery segmentation approach.

Automated summary

The study proposes a fully automatic carotid artery segmentation method combining Affinity propagation and DBSCAN, which was evaluated on a database of 361 images and resulted in reinforced results in longitudinal B-mode ultrasound images. The proposed approach showed an improved accuracy of 12% compared to manual segmentation and 15% compared to segmentation by affinity propagation and DBSCAN individually. The average Root Mean Square Error (RMSE) was 110 +/- 44 μm.