光学学报, 2018, 38 (2): 0215003, 网络出版: 2018-08-30  

基于贝叶斯理论的手臂静脉线跟踪方法 下载: 776次

Line Tracking Method of Arm Vein Based on Bayesian Theory
作者单位
南京航空航天大学自动化学院, 江苏 南京 211106
摘要
提出了一种基于贝叶斯理论检测血管边界的手臂静脉线跟踪方法,该方法自动选取初始种子点,避免人工干预。跟踪血管时,将血管结构分为正常型、分支型和交叉型3种。每次迭代时综合考虑血管的横向和纵向特性。由于短距离内血管近似为直线,可利用多尺度直线模板对图像进行滤波,得到像素点的直线强度。使用高斯模型拟合血管横截面的灰度分布,基于贝叶斯最大后验概率准则,确定可能性最大的血管结构,从而得到局部血管的边界点、中心点、直径和方向。实验结果表明:与传统的阈值分割法和重复线跟踪法相比,所提方法提取出的手臂静脉线更准确、更全面,且具有较好的噪声稳健性。
Abstract
A line tracking method of arm vein is proposed based on Bayesian theory, and the method can detect vascular boundary. The method can automatically select initial seed points, avoiding artificial disturbance. In the vessel tracking, the vessel structures are divided into normal, bifurcation and crossing types. For each iteration, the algorithm takes longitudinal and horizontal characteristics of vessels into account. Because blood vessels within a short distance are approximately a straight line, a multi-scale line template is applied on the image filtering to obtain line strength of each pixel. A Gaussian model is used to fit the gray distribution of vessel along the cross section. The most possible vessel structure is determined based on Bayesian maximum posteriori probability criterion. Therefore, the edge points, central points, diameter and direction of local vessel can be acquired. The experiment results show that, compared with traditional threshold segmentation method and repeated line tracking method, the proposed method performs better in identification accuracy, comprehensiveness, and robustness to noise.

高昊昇, 唐超颖, 陈晓腾, 余笑. 基于贝叶斯理论的手臂静脉线跟踪方法[J]. 光学学报, 2018, 38(2): 0215003. Haosheng Gao, Chaoying Tang, Xiaoteng Chen, Xiao Yu. Line Tracking Method of Arm Vein Based on Bayesian Theory[J]. Acta Optica Sinica, 2018, 38(2): 0215003.

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