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

S Latha,R Kumar,Dhanalakshmi Samiappan,P Muthu

doi:10.1007/s40846-020-00586-9

Abstract

PurposeB-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.MethodsA 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.ResultsThe 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 µm.ConclusionExtracted edge points are used for clustering in a fully automated carotid artery segmentation approach.

Highlights

Ultrasound imaging is a promising imaging modality in understanding the characteristics of carotid arteries and related vascular problems
The comparison of the performance of the proposed affinity propagation and DBSCAN with Particle Swarm Optimization (PSO) and Gradient Vector Flow (GVF) segmentation approaches is studied
The snake formation depends on the initial contour selection since it grows from that boundary point

Summary

Introduction

Ultrasound imaging is a promising imaging modality in understanding the characteristics of carotid arteries and related vascular problems. Modified semi-supervised affinity propagation approach with autonomous module study were developed for clustering the ECG heart beat recordings [14] By this method, the labelled samples were used as seeds to initialize the cluster centers in k-means algorithm. Based on a Fuzzy Statistical Similarity (FSS) quantity, affinity propagation based clustering was proposed which shows faster implementation, least error and demonstrates how near two pixel vectors look alike [19] In this method, the data points are taken as applicant exemplars and permits soft data till a subset of data points become exemplars. With extracted edges as core and border points, a local cluster based segmentation with DBSCAN is proposed Both the algorithms are made parameter free making it not relying on initial assumptions and ground truth.

Carotid Artery Ultrasound Imaging

Affinity Propagation Based Segmentation

DBSCAN Based Clustering

PSO and GVF Based Segmentation

Dataset and Performance Measures

Results and Discussion

Conclusion