基于多尺度自适应滤波的DSA血管增强

计算机工程与应用 ›› 2015, Vol. 51 ›› Issue (14): 179-185.

基于多尺度自适应滤波的DSA血管增强

张新红1，张帆2，3，崔延斌3

1.河南大学软件学院，河南开封 475001
2.河南大学图像处理与模式识别研究所，河南开封 475001
3.河南大学计算机与信息工程学院，河南开封 475001

出版日期:2015-07-15 发布日期:2015-08-03

Blood vessel enhancement algorithm for DSA images based on adaptive multi-scale filtering

ZHANG Xinhong1, ZHANG Fan2，3, CUI Yanbin3

1.School of Software, Henan University, Kaifeng, Henan 475001, China
2.Institute of Image Processing and Pattern Recognition, Henan University, Kaifeng, Henan 475001, China
3.School of Computer & Information Engineering, Henan University, Kaifeng, Henan 475001, China

Online:2015-07-15 Published:2015-08-03

摘要/Abstract

摘要： 数字减影血管造影（DSA）技术在血管疾病的诊断和治疗中起着重要的作用。由于血管结构的几何复杂性, 准确地检测血管仍然是一个难题。为了提高DSA图像的视觉质量，提出了一种基于多尺度自适应滤波的DSA血管增强算法。新的血管增强算法基于多尺度空间理论，不仅采用Hessian矩阵的特征值，而且利用特征向量之间的角度进行DSA血管增强。滤波器的参数可以根据图像本身的特征自适应地进行调整。在多尺度迭代过程中，噪声往往被放大，因此滤波器中增加了噪声消除的设计。实验结果表明，该算法具有良好的DSA血管增强性能，可以有效地过滤图像背景和非血管结构，并避免了增强过程中可能发生的血管变形。

关键词: 数字减影血管造影, 血管增强, 多尺度滤波, Hessian矩阵

Abstract: Digital Subtraction Angiography （DSA） plays a significant role in the diagnosis and treatment of blood vessel diseases. However, because of the geometrical complexity of blood vessel structures, detecting blood vessels accurately still remains a difficult problem. In this paper, a blood vessel enhancement algorithm is proposed. The new blood vessel enhancement algorithm is based on multi-scale space theory and Hessian matrix. Not only the eigenvalues of Hessian matrix but also the angles between eigenvectors are utilized for the blood vessel enhancement of DSA. The filter parameters are decided adaptively. Eigenvalues of the Hessian matrix are also used for the noise elimination. Experimental results show that the proposed algorithm has a good performance in blood vessel enhancement of DSA images. Image background and non-vascular structures are eliminated effectively and deformation of blood vessels occurred in the enhancement process is avoided.

Key words: digital subtraction angiography, vessel enhancement, multi-scale filtering, Hessian matrix

张新红1，张帆2，3，崔延斌3. 基于多尺度自适应滤波的DSA血管增强[J]. 计算机工程与应用, 2015, 51(14): 179-185.

ZHANG Xinhong1, ZHANG Fan2，3, CUI Yanbin3. Blood vessel enhancement algorithm for DSA images based on adaptive multi-scale filtering[J]. Computer Engineering and Applications, 2015, 51(14): 179-185.

[1]	谭建豪1，张文涛1，李晓龙1，余绍德2，谢耀钦2. 一种改进的3D血管增强算法[J]. 计算机工程与应用, 2016, 52(15): 153-157.
[2]	张新红1，张　帆2，3，崔延斌3. 基于误差扩散和均值漂移的DSA特征点选择[J]. 计算机工程与应用, 2015, 51(13): 157-161.
[3]	彭辉1，2，文友先2，翟瑞芳1，罗俊1，刘善梅1. 结合SURF算子和极线约束的柑橘立体图像对匹配[J]. 计算机工程与应用, 2011, 47(8): 157-160.
[4]	夏毓鹏¹，王昕²，胡锋². 光流场算法中优化图像梯度数据可信度方法[J]. 计算机工程与应用, 2010, 46(4): 163-165.
[5]	鲍江宏,李炯城. 一种新型高效的计算机寻优算法[J]. 计算机工程与应用, 2007, 43(35): 49-51.
[6]	高园园¹，吕庆文¹，郭宏²，冯前进¹，陈武凡¹. 一种新的肺结节检测算法[J]. 计算机工程与应用, 2007, 43(23): 198-199.