Research on Finger Vein Image Segmentation and Blood Sampling Point Location in Automatic Blood Collection

Abstract

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%.

Dorsal Hand Vein Image Enhancement Using Fusion of CLAHE and Fuzzy Adaptive Gamma

“…Vein enhancement based on spatial domain filtering such as adaptive learning Gabor filters [ 7 ], 2D Gabor filters [ 21 ], and adaptive Gabor filters [ 22 ] has been successfully applied for vein images. The Gaussian directional filter [ 23 ], guided filter with single-scale Retinex [ 24 ], unsharp masking [ 11 ], and Gaussian matched filters [ 25 ] have also been used to boost up the local contrast in vein patterns.…”