School of Communication and Information Engineering, Chongqing University of Posts and Telecommunications, Chongqing 400065, China.
Foundation Department, Chongqing Medical and Pharmaceutical College, Chongqing 401331, China.
Sensors (Basel). 2020 Dec 28;21(1):132. doi: 10.3390/s21010132.
In the fingertip blood automatic sampling process, when the blood sampling point in the fingertip venous area, it will greatly increase the amount of bleeding without being squeezed. In order to accurately locate the blood sampling point in the venous area, we propose a new finger vein image segmentation approach basing on Gabor transform and Gaussian mixed model (GMM). Firstly, Gabor filter parameter can be set adaptively according to the differential excitation of image and we use the local binary pattern (LBP) to fuse the same-scale and multi-orientation Gabor features of the image. Then, finger vein image segmentation is achieved by Gabor-GMM system and optimized by the max flow min cut method which is based on the relative entropy of the foreground and the background. Finally, the blood sampling point can be localized with corner detection. The experimental results show that the proposed approach has significant performance in segmenting finger vein images which the average accuracy of segmentation images reach 91.6%.
在指尖自动采血过程中，当采血点位于指尖静脉区域时，无需挤压就会大大增加出血量。为了准确地定位静脉区域的采血点，我们提出了一种新的基于 Gabor 变换和高斯混合模型 (GMM) 的指静脉图像分割方法。首先，可以根据图像的差分激励自适应设置 Gabor 滤波器参数，并使用局部二值模式 (LBP) 融合图像的同尺度多方向 Gabor 特征。然后，通过 Gabor-GMM 系统实现指静脉图像分割，并基于前景和背景的相对熵，采用最大流最小割方法进行优化。最后，通过角点检测定位采血点。实验结果表明，该方法在指静脉图像分割方面具有显著的性能，分割图像的平均准确率达到 91.6%。
