基于多尺度模糊聚类与DGVF模型分割颈动脉超声造影图像

CEUS Image Segmentation of Carotid Arteries Using Multi-scale Fuzzy Clustering and DGVF Model

超声造影(contrast-enhanced ultrasound,CEUS)图像在血管疾病诊断与治疗中有很高的应用价值,其中通过提取颈动脉CEUS图像中的血管边界对血管形态及弹性等属性进行测量具有重要意义.由医生手工勾勒血管轮廓耗时耗力,且重复性差、主观性强,而传统计算机分割方法因受到图像中斑点噪声的干扰而存在鲁棒性差和初始化难两大问题.首先,结合多尺度模糊聚类方法与粒子群优化算法提取血管的粗略轮廓,以此作为方向梯度矢量流(directional gradient vector flow,DGVF)模型的初始轮廓;然后,对轮廓进行形变收敛至最终结果.通过分割来自14例患者的48张颈动脉CEUS图像的实验,结果表明所提出的方法优于传统方法,能自动、精确地提取颈动脉CEUS图像中的血管边界.

Contrast-enhanced ultrasound （CEUS） is of great value for the diagnosis and treatment of vascular diseases. Extraction of carotid arterial contours is important for the measurement of morphological and elastic properties of arteries. Since manually tracing of arterial contours is time-consuming, subjective, and unrepeatable, computer-aided methods are required. However, speckle noise in the CEUS images causes poor robustness and difficult initialization in traditional computer-aided image segmentation methods. This paper integrates multi-scale fuzzy C-means clustering with particle swarm optimization to extract coarse boundaries of carotid arteries. Then boundaries are used as initial contours of the directional gradient vector flow （DGVF） model, and deform them until convergence to get final refined contours. Experimental results on 48 CEUS images from 14 patients show that the proposed method is superior to the traditional method, and can automatically and accurately extract boundaries of carotid arteries in CEUS images.