Active Contour-Based Method for Finger-Vein Image Segmentation

Abstract

Suffering from uneven illumination and variation of finger position, it is still a tough challenge to effectively distinguish the vein networks and nonvenous regions in a finger-vein image. Methods based on active contour have achieved an excellent result in medical image segmentation, despite facing several challenges such as vulnerable to the initial contour and prone to local minimum. In this article, we propose a novel method which is effective for finger-vein image segmentation based on active contour. Since venous and nonvenous areas in captured finger-vein images are hard to distinguish, we design a dehazing algorithm and an edge fitting term to improve the segmentation procedure. Moreover, we employ the kernel fuzzy C-means (KFCM) algorithm to conduct the initialization, which is able to solve the problem that the active contour-based methods are susceptible to initial contours. The experimental results show that compared with latest methods, the proposed method achieves a better performance in segmenting finger-vein images and is able to improve the recognition accuracy of finger-vein identification system.