基于血液斑点噪声抑制和T-Snake模型的血管内超声图像边缘提取

血管内超声(IVUS)图像冠状动脉血管壁内、外膜的边缘提取对冠状动脉疾病的诊断和治疗有着重要意义。为了更好地抑制血管内超声图像的血液斑点噪声,首先采用一种时/空滤波方法对IVUS图像进行降噪预处理,以抑制其严重的血液斑点噪声;然后,为了更好地提取血管边缘,提出了一种改进的自适应形变模型,并基于该改进的自适应形变模型(T-Snake模型)给出了一种IVUS图像冠状动脉血管壁内、外膜边缘的提取方法。实验结果表明,该边缘提取方法有着较高的准确性和可靠性,对IVUS序列图像处理的可重复性和鲁棒性较好;也表明了改进的T-Snake模型的可实现性,以及IVUS图像血液斑点噪声抑制方法的有效性。

Edge Detection of IVUS Images Based on Blood Speckle Noise Reduction and the T-Snake Model

Edge detection of intravascular ultrasound(IVUS) images is important for the diagnosis and treatment of the coronary artery disease.We adopt a spatial/temporal adaptive filtering method to reduce noise in IVUS images which contain severe blood speckle noise.An improved topologically adaptable Snakes madel(T-Snake) is proposed in the paper,based on which a method for automatically detecting the edge of IVUS image is presented.Experiments show that the edge detection method is accurate,reproducible and robust for sequential IVUS frames and also the improved T-Snake is effective and realizable and the presented method for reducing the blood speckle noise is effective as well.