手指静脉图像血管网分形修复方法

Vascular Network Restoration Method for Finger-vein Images Based on Fractal

由于生物组织对近红外光具有高散射效应,手指静脉红外透射成像质量往往较差,血管网络存在信息残缺。为了解决指静脉血管残缺问题,本文提出一种基于分形的手指静脉红外图像血管网络修复方法。首先,利用多尺度Gabor滤波对指静脉图像增强,进而提取血管骨架;其次,利用Gabor滤波得到的方向特征对血管网络进行结构预修复;接着采用K均值聚类方法提取指静脉结构的父子血管长度比特征,将其作为一种分形特征;然后,利用该特征计算缺损血管长度;最后,建立血管点移动模板,通过统计相邻血管点位置移动概率实现血管形态模拟及网络修复。实验结果表明,本文方法可以实现指静脉图像局部残缺区域的修复,从而提高手指静脉识别精度。

Due to the high scattering of near-infrared light in biological tissue,the quality of finger-vein images is often undesirable and the vein network is incomplete.To solve the problem of vascular network coloboma in finger-vein IR images and achieve higher accuracy in finger-vein recognition,a method based on fractal is proposed to restore the finger-vein network.First,the finger-vein images are enhanced by using multi-scale Gabor filters to extract the skeletons of veins.The following steps are based on the skeletons extracted.Second,since part of the vessel segments are lost caused by the binarization in the processing above,the 8-orientation images obtained by Gabor filtering are used to prerestore the vascular structure.Based on the prerestored results,the ratio of branch length to the length of the parent vessel,as a kind of fractal feature,is extracted by using K-means algorithm.Then,the length of the lost blood vessel is calculated based on the fractal feature.Finally,with a movement template of vessel points,the finger vein is simulated by calculating the movement probability of neighbor vessel points.The experimental results show that the proposed method is effective to restore the locally incomplete region of finger-vein network.The proposed method also achieves higher accuracy in finger-vein recognition.