Vein pattern extraction based on vectorgrams of maximal intra-neighbor difference

In this paper, a vein pattern extraction method is proposed for biometric purposes. First, we utilize a maximal intra-neighbor difference (MIND) vector of all pixels in the original image to represent the relationship between each pixel and its neighborhood. Based on the MIND vectorgram (MINDVG), we define a maximal intra-neighbor vector difference (MIVND) as an index to unveil the preliminary vein pattern. Finally, we use an adaptive threshold to extract the venation pattern. The advantage of this method is that, by combining the features of vein imaging and the spatial properties of the MINDVG, the algorithm can efficiently overcome the negative factors of inhomogeneous thickness and blurry boundaries in vein imaging without preprocessing. Experiments on several images show that this method can directly extract intact and clear vein patterns with minimal noise. Therefore, the proposed algorithm has been validated in vein pattern extraction.

► We build a MIND vectorgram (MINDVG) that represents the relationship between pixel and its neighbourhood.
► We calculate maximum intra-neighbor vector difference (MIVND) to unveil the preliminary vein pattern.
► The method combines the features of vein image and spatial properties of MINDVG.
► The method overcomes the negative factors brought by inhomogeneous thickness and blurry boundary.