手背静脉图像的采集过程中，由于图像采集设备、光照、皮下脂肪厚度等因素的影响，手背静脉图像的对比度比较低，同时图像噪声严重影响静脉提取。针对此问题，本文提出了一种基于静脉灰度值特征的图像分割与对比度增强算法。首先提取ROI(有效的感兴趣区域)和对ROI 进行维纳滤波；然后采用新的图像分割算法对静脉图像进行静脉提取，利用8-邻接内边界跟踪方法和形态学处理方法对静脉二值图像进行去噪；最后将ROI 与去噪后的图像进行加权叠加得到对比度增强的静脉图像。实验结果表明，通过采用基于静脉灰度值特征的图像分割算法可以很好地获取到静脉脉络，最终可以获得高对比度的静脉图像。

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.