In the process of collecting hand vein images, due to the influence of image acquisition equipment, illumination and subcutaneous fat thickness, the contrast of hand vein images is relatively low. Meanwhile, vein extraction is seriously affected by image noise. To solve this problem, an algorithm of image segmentation and contrast enhancement based on features of venous gray value is proposed in this paper. Firstly, effective region of interest (ROI) is extracted and filtered through Wiener filtering. Secondly, a new image segmentation algorithm is obtained to extract vein image. The venous binary image is denoised by an 8-adjacent inner boundary tracking method and morphological processing. Finally, contrast-enhanced venous images are obtained by weight stack of the ROI and denoised images. The experiments results show that intravenous veins can be obtained perfectly by using the image segmentation algorithm based on features of venous gray value. Moreover, the high contrast venous images can be obtained.
Research on image segmentation algorithm based on features of venous gray value
First published at:Dec 01, 2018
1 Li W, Yuan W Q. Imaging quality analysis on palm vein under different wavelengths near-IR[J]. Computer Engineering and Applications, 2011, 47(30): 15-18. DOI:10.3778/j.issn.1002-8331.2011.30.005
李威, 苑玮琦.不同波长近红外光下手掌静脉图像质量分析[J].计算机工程与应用, 2011, 47(30): 15-18. DOI:10.3778/j.issn.1002-8331.2011.30.005
2 Cai C F, Ren J Y. Contrast enhancement of hand vein images based on histogram equalization[J]. Journal of Computer Applications, 2013, 33(4): 1125-1127.
3 Yang X P, Cai C F, Pan H, et al. Research on Pretreatment Algorithm of Hand Vein Image[J]. China Medical Devices, 2013, 28(10): 20-23. DOI:10.3969/j.issn.1674-1633.2013.10.005
杨晓鹏, 蔡超峰, 潘珩, 等.手背静脉图像预处理算法研究[J].中国医疗设备, 2013, 28(10): 20-23. DOI:10.3969/j.issn.1674-1633.2013.10.005
4 Wang H B, Tao L. Novel algorithm for enhancement of hand vein images based on adaptive filtering and retinex method[C]//Proceedings of 2012 IEEEInternational Conference on Information Science and Technology, 2012: 857-860.
5 Hu X Y. Novel algorithm for hand vein image enhancement and segmentation[J]. Computer Knowledge and Technology, 2014, 10(21): 5080-5082.
6 Wang Z D, Sun H X, Deng Y D, et al. Image processing algorithm for hand vein recognition[J]. Journal of University of Science and Technology Liaoning, 2010, 33(5): 499-502, 508. DOI:10.3969/j.issn.1674-1048.2010.05.013
王镇东, 孙红星, 邓永娣, 等.手背静脉识别的图像处理算法[J].辽宁科技大学学报, 2010, 33(5): 499-502, 508. DOI:10.3969/j.issn.1674-1048.2010.05.013
7 Funt B V, Ciurea F, Mccann J J. Retinex in Matlab[C]//Color and Imaging Conference, 2000(10): 112-121.
8 Otsu N. A. Threshold Selection Method from Gray-Level Histograms[J]. IEEE Trans. Syst. Man. & Cybern, 2007, 9(1): 62-66.
9 Han X. Research on algorithm for human dorsal hand vein recognition[D]. Changchun: Jilin University, 2007.
10 Suresh K, Papendra K, Manoj G, et al. Performance Comparison of Median and Wiener Filter in Image De-noising[J]. International Journal of Computer Applications, 2010, 12(4): 24-28.
11 Milan Sonka, Vaclav Hlavac, Roger Boyle. Image processing, Analysis and Machine Vision[M]. Beijing: Tsinghua University Press, 2011: 230-231. Milan Sonka, Vaclav Hlavac, Roger Boyle.
Supported by National Natural Science Foundation of China (61505192) and Natural Science Foundation of Zhejiang Province (LQ15F050004)
Get Citation: Wang Dinghan, Feng Guilan, Wang Xiong, et al. Research on image segmentation algorithm based on features of venous gray value[J]. Opto-Electronic Engineering, 2018, 45(12): 180066.