摘要
为解决血管内超声(IVUS)图像中严重的血液斑点噪声影响边缘提取方法的有效性问题,采用一种时/空滤波方法对IVUS图像进行降噪预处理,基于活动轮廓模型(Snake模型)和IVUS图像的边缘对比度特征量,利用Hopfield神经网络并结合模拟退火算法自动提取IVUS图像的冠脉血管壁内、外膜边缘.实验结果表明,该方法对序列IVUS图像处理有较好的可重复性和鲁棒性.
Blood speckle noise in the IVUS image would affect： the validity of edge detection badly. A temporal/spatial filtering method was used to preprocess the IVUS image to restrain the noise. Then a method to automatically detect the intima and ectoblastic edge of IVUS image was presented. This method is based on the active contour model （Snakes model） and the contrast properties of 1VUS image, which makes use of the Hopfield NN and simulated annealing algorithm. Experiments show that the method is accurate, repeatable and robust for sequential IVUS frames.
出处
《山东大学学报（工学版）》
CAS
2006年第5期44-48,57,共6页
Journal of Shandong University（Engineering Science）
基金
国家自然科学基金资助项目(60571040)
山东省优秀中青年科学家奖励基金项目(2005BS1006)
关键词
血管内超声
斑点噪声
时/空滤波
边缘提取
活动轮廓模型
intravascular ultrasound
speckle noise
temporal/spatial filtering
edge detection
the active contour model
