Multiple Features Decomposition for Subcutaneous Vein Extraction and Measurement

Abstract

Subcutaneous veins should be accurately segmented to satisfy the emerging demands in biometrics-based identity recognition, automated intravenous injection, and vascular surgical navigation. This paper proposes a novel segmentation method for extracting vascular vein structures from near-infrared images based on multiple feature classification. First, the isotropic undecimated wavelet transform in combination with the Hessian function is used to enhance the visibility of vascular structures. An approximated vascular structure is extracted by multiple-scale features based on the enhanced image. The establishment of the mapping relationship between the sketch image and source image spaces is assisted by mending and fitting procedures. Vascular features are also extracted from the enhanced contour image by a matched filter. Furthermore, the image can be classified into vascular, skin, and blurred regions by a K-means algorithm. The final vascular segmentation outcome is a refined structure. Segmentation results demonstrated that the proposed method is robust for subcutaneous vein images with different blur scales, where the completeness and smoothness of the extracted vein structure are retained. The proposed method is accurate and completely automatic, with potential applications in biometric feature recognition and intravenous injection.