Hand Vein Image Enhancement Based on Multi-Scale Top-Hat Transform

Traditional image contrast enhancement methods originally cannot improve the quality of vein images and may also import some unknown noise resulting in low recognition rate. To overcome the abovementioned disadvantages, the paper proposes an enhancement method based on the morphological filtering theory including three main procedures. Firstly, the algorithm extract the vein Region Of Interest (ROI), and then adopting the improved White Top-Hat transform (WTH) and Black Top-Hat transform (BTH) methods to get the features of vein in detail in both white and black pattern (vein information and background information); Secondly, to construct the filtering function with the self-designed controlling operator, representing the gradient changes of the vein edges, which well reflects the importance of local detail in multi-scale pattern; Finally, traditional nonlinear gray-level transformation function is imported with modality to the parameters to realize the gray normalization. We perform rigorous experiments with the proposed method and other state-of-the-art enhancement methods on the self-built dorsal vein image databases, and the experimental results illustrate that the multiscale top-hat theory-based enhancement methods improve the contrast of hand vein images with restrictions on the possibility of enhancement on existing noise information.