基于snake模型的IVUS图像序列三维分割方法

针对血管内超声（Intravascular Ultrasound,IVUS）图像序列中血管壁内外膜轮廓的提取问题,提出一种基于snake模型的三维并行分割方法。首先,对原始图像进行滤除噪声和抑制环晕伪像等预处理。然后,获取IVUS图像序列的四个纵向视图,并从中提取出内腔边界和中-外膜边界。通过将这些边界曲线映射到各帧IVUS图像中,得到横向视图中的初始轮廓。最后,将该初始轮廓作为snake模型的初始形状,通过使snake能量函数最小,模型不断变形,最终得到各帧IVUS图像中的内腔和中-外膜边界。该方法可实现对IVUS图像序列的并行分割,与二维串行分割方法相比,可大大提高处理效率。采用大量临床图像数据的实验结果证明该方法可自动、快速、可靠的完成IVUS图像序列的分割。

3D Segmentation of IVUS Image Sequence Based on Snake Model

A 3D parallel method is proposed for segmenting intravascular ultrasound（IVUS） image sequence to extract vessel wall borders based on snake model.Firstly,original images are preprocessed to reduce possible noises and suppress ring-down artifacts.Then,four IVUS longitudinal cuts are obtained where lumen and media-adventitia borders are detected.Once these borders are mapped onto each cross-sectional slice,initial contours in each IVUS frame are obtained.Finally,these initial contours evolve continuously through minimization of a snake energy function until stop at target borders.Consequently,segmentation of each IVUS frame is implemented simultaneously and the efficiency is greatly raised compared with 2D sequential segmentation approaches.The proposed method is experimentally evaluated in large datasets of IVUS images clinically derived from human coronary arteries.Results demonstrate that IVUS images can be automatically,quickly and reliably segmented with the method.