A Novel Method for Low-Contrast and High-Noise Vessel Segmentation and Location in Venipuncture

Blood sampling is the most common medical technique, and vessel detection is of crucial interest for automated venipuncture systems. In this paper, we propose a new convex-regional-based gradient model that uses contextually related regional information, including vessel width size and gray distribution, to segment and locate vessels in a near-infrared image. A convex function with the interval size of vessel width is constructed and utilized for its edge-preserving superiority. Moreover, white and linear noise independences are derived. The region-based gradient decreases the number of local extreme in the cross-sectional profile of the vessel to realize its single global minimum in a low-contrast, noisy image. We demonstrate the performance of the proposed model via quantitative tests and comparisons between different methods. Results show the advantages of the model on the continuity and smoothness of segmented vessel. The proposed model is evaluated with receiver operating characteristic curves, which have a corresponding area under the curve of 88.8%. The proposed model will be a powerful method in automated venipuncture system and medical image analysis.