A novel algorithm of dorsal hand vein image segmentation by integrating matched filter and local binary fitting level set model

• Published in: 2020 7th International Conference on Information Science and Control Engineering (ICISCE) pp. 81 - 85
• Main Authors: Guo, Ziyang, Ma, Yao, Min, Xiaolin, Li, Hui, Liu, Qingyi, Han, Chao, Yang, Guang, Bai, Peirui, Ren, Yande
• Format: Conference Proceeding
• Language: English
• Published: IEEE 01.12.2020
• Subjects: Control engineering; dorsal hand vein; Fitting; Image segmentation; Information science; Level set; matched filter; Matched filters; the LBF model; Veins
• DOI: 10.1109/ICISCE50968.2020.00027

The performance of dorsal hand vein image segmentation is limited due to low contrast and intensity inhomogeneity. In this paper, a novel method is proposed by integrating matched filter and local binary fitting level set model with the aim of overcoming the fault or incomplete segmentation in dorsal hand vein image. Following is the main work and contributions of this paper. First, 12-direction matched filters are adopted to enhance the vein patterns. Then, the local binary fitting level set model is introduced to segment the image enhanced by the first step. Third, a spurious vascular removal solution is presented to reduce the interference of metacarpal bones. 380 dorsal hand vein images collected from 69 subjects are used to evaluate the performance of the proposed algorithm. Compared with 5 existing vein segmentation methods, the proposed method achieves superior accuracy and shows great potential in image segmentation.