手指静脉红外图像血管网络修复新方法
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摘要
由于手指静脉位于皮下,手指中的生物组织、手指解剖结构、皮肤结构成像特性等固有原因都给手指静脉成像造成不利影响。针对手指静脉图像中普遍存在的局部血管残缺问题,首次提出一种指静脉红外图像血管网络修复方法。首先,利用多尺度Gabor滤波对手指静脉图像进行增强,减少图像整体退化性模糊;然后,对指静脉图像进行二值化并提取血管骨架网,以便对血管网络缺损位置进行判断;再将提取的血管骨架端点、二分叉点作为血管骨架网络修复的源点,根据最小路径原则实现手指静脉图像血管骨架网络修复;最后,将Gabor增强方向图作为约束条件,复原血管网络的管径信息得到修复后的手指静脉二值化图像。实验结果表明:该方法可以实现手指静脉图像局部血管网络残缺修复,得到更加完整、稳定的血管网络结构,利用修复后的图像可以进一步提高手指静脉识别精度。
For the finger-vein is under the skin, there are many inherent disadvantages for its imaging,such as biological tissues in the finger, anatomical structure, and the imaging character of skin. A novel method was proposed to solve the problem of vascular network coloboma in finger-vein IR images.Firstly, the finger-vein images were enhanced by multi-scale Gabor filter to reduce the overall image blurring. Then, the vascular skeleton network was extracted based on binarized images so as to locate the coloboma position accurately. Thirdly, the end point and the bifurcation point were extracted from the vascular skeleton network as the original point of restoration. The coloboma of the vascular skeleton network was reconstructed according to minimal path principle. Finally, the diameter of vascular network was recovered by using the Gabor directional image as a constraint. The experimental results show that this method can be used to restore local lost of vascular network and a more complete and more stable vascular network. The recognition accuracy of finger-vein images can be further improved by using the reconstructed image.
For the finger-vein is under the skin, there are many inherent disadvantages for its imaging,such as biological tissues in the finger, anatomical structure, and the imaging character of skin. A novel method was proposed to solve the problem of vascular network coloboma in finger-vein IR images.Firstly, the finger-vein images were enhanced by multi-scale Gabor filter to reduce the overall image blurring. Then, the vascular skeleton network was extracted based on binarized images so as to locate the coloboma position accurately. Thirdly, the end point and the bifurcation point were extracted from the vascular skeleton network as the original point of restoration. The coloboma of the vascular skeleton network was reconstructed according to minimal path principle. Finally, the diameter of vascular network was recovered by using the Gabor directional image as a constraint. The experimental results show that this method can be used to restore local lost of vascular network and a more complete and more stable vascular network. The recognition accuracy of finger-vein images can be further improved by using the reconstructed image.
引文
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[16] Peng Jinjin. Multimodal finger feature recognition based on traditional granulation[D]. Tianjin:Civil Aviation University of China, 2015.(in Chinese)
[17] Wen Mengna. Research on finger-vein image segmentation and recognition based on CNN[D]. Tianjin:Civil Aviation University of China, 2018.(in Chinese)
[2] Jia Guimin, Li Shuyi, Yang Jinfeng, et al. Novel invariant feature encoding method for finger-vein IR images[J].Infrared and Laser Engineering, 2018, 47(9):0926006.(in Chinese)
[3] Yang J, Zhang X. Feature-level fusion of fingerprint and finger-vein for personal identification[J]. Pattern Recogn Lett, 2012, 33(5):623-628.
[4] Yang G, Xi X, Yin Y. Finger vein recognition based on a personalized best bit map[J]. Sensors, 2012, 12(12):1738-1757.
[5] Liu F, Yang G, Yin Y, et al. Singular value decomposition based minutiae matching method for finger vein recognition[J]. Neurocomputing, 2014, 145(5):75-89.
[6] Kono M, Ueki H, Umemura S. Near-infrared finger vein patterns for personal identification[J]. Appl Opt, 2002, 41(35):7429-36.
[7] Lee E C, Park K R. Image restoration of skin scattering and optical blurring for finger vein recognition[J]. Optics&Lasers in Engineering, 2011, 49(7):816-828.
[8] Yang J, Yang J. Multi-channel gabor filter design for fingervein image enhancement[C]//2009 Fifth International Conference on Image and Graphics, 2009:87-91.
[9] Joshi V S. Analysis of retinal vessel networks using quantitative descriptors of vascular morphology[D]. USA:University of Iowa, 2012:48-59.
[10] Al-Diri B, Hunter A, Steel D, et al. Joining retinal vessel segments[C]//IEEE International Conference on Bioinformatics&Bioengineering, 2008:1-6.
[11] Caliva F, Hunter A, Chudzik P, et al. A fluid-dynamic based approach to reconnect the retinal vessels in fundus photography[C]//International Conference of the IEEE Engineering in Medicine&Biology Society, 2017:360-364.
[12] Yang J, Shi Y. Finger-vein ROI localization and vein ridge enhancement[J]. Pattern Recogn Lett, 2012, 33(12):1569-1579.
[13] Yang J, Shi Y. Finger-vein network enhancement and segmentation[J]. Pattern Anal Appl, 2014, 17(4):783-797.
[14] Mei C, Xiao X, Liu G, et al. Feature extraction of fingervein image based on morphologic algorithm[C]//2009 Sixth International Conference on Fuzzy Systems&Knowledge Discovery, 2009:407-411.
[15] Yu J, Li Y. Improving Hilditch thinning algorithms for text image[C]//2009 International Conference on E-Learning, EBusiness, 2009:76-79.
[16] Peng Jinjin. Multimodal finger feature recognition based on traditional granulation[D]. Tianjin:Civil Aviation University of China, 2015.(in Chinese)
[17] Wen Mengna. Research on finger-vein image segmentation and recognition based on CNN[D]. Tianjin:Civil Aviation University of China, 2018.(in Chinese)