Faculty of Electrical and Electronic Engineering Technology, Universiti Malaysia Pahang, Pekan 26600, Pahang, Malaysia.
School of Electrical and Electronic Engineering, Universiti Sains Malaysia, Nibong Tebal 14300, Pulau Pinang, Malaysia.
Sensors (Basel). 2021 Sep 27;21(19):6445. doi: 10.3390/s21196445.
Enhancement of captured hand vein images is essential for a number of purposes, such as accurate biometric identification and ease of medical intravenous access. This paper presents an improved hand vein image enhancement technique based on weighted average fusion of contrast limited adaptive histogram equalization (CLAHE) and fuzzy adaptive gamma (FAG). The proposed technique is applied using three stages. Firstly, grey level intensities with CLAHE are locally applied to image pixels for contrast enhancement. Secondly, the grey level intensities are then globally transformed into membership planes and modified with FAG operator for the same purposes. Finally, the resultant images from CLAHE and FAG are fused using improved weighted averaging methods for clearer vein patterns. Then, matched filter with first-order derivative Gaussian (MF-FODG) is employed to segment vein patterns. The proposed technique was tested on self-acquired dorsal hand vein images as well as images from the SUAS databases. The performance of the proposed technique is compared with various other image enhancement techniques based on mean square error (MSE), peak signal-to-noise ratio (PSNR), and structural similarity index measurement (SSIM). The proposed enhancement technique's impact on the segmentation process has also been evaluated using sensitivity, accuracy, and dice coefficient. The experimental results show that the proposed enhancement technique can significantly enhance the hand vein patterns and improve the detection of dorsal hand veins.
增强捕获的手部静脉图像对于许多目的都是至关重要的，例如准确的生物识别和便于进行医疗静脉穿刺。本文提出了一种基于对比度受限自适应直方图均衡化（CLAHE）和模糊自适应伽马（FAG）加权平均融合的改进手部静脉图像增强技术。该技术应用于三个阶段。首先，CLAHE 对图像像素进行局部灰度级强度处理，以增强对比度。其次，将灰度级强度全局转换为隶属度平面，并使用 FAG 算子进行修改，以达到相同的目的。最后，使用改进的加权平均方法融合 CLAHE 和 FAG 的结果图像，以获得更清晰的静脉模式。然后，使用一阶导数高斯滤波器（MF-FODG）进行静脉模式分割。该技术在自采集的手背静脉图像以及 SUAS 数据库的图像上进行了测试。基于均方误差（MSE）、峰值信噪比（PSNR）和结构相似性指数测量（SSIM）等各种其他图像增强技术对提出的技术进行了性能比较。还使用灵敏度、准确性和骰子系数评估了该增强技术对分割过程的影响。实验结果表明，所提出的增强技术可以显著增强手部静脉模式，提高手背静脉的检测效果。
