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Abstract. In view of modern clinical treatment and care, venipuncture has a very important position, but in order to train a medical staff with high level skill of puncture need to spend a lot of time, human and material resources. For this reason, in this paper, an automatic system that can achieve venipuncture is proposed, which can replace the medical staff and achieve the puncture process. The automatic venipuncture system, combined with NIR imaging and ultrasound imaging technology, the overall plane information is obtained by NIR image, depth information and fine positioning of the plane information is obtained by ultrasound image. By way of image processing, the NIR image is enhanced and segmented, getting the spatial location of the vein. And the most suitable blood vessel segments were selected by piecewise straight fitting. Then the location ultrasonic probe should be placed is determined. Then the ultrasound image with the blood vessel in the vicinity of the longitudinal centreline can be obtained. All the information of blood vessel have been obtained, then the machine can be driven to achieve 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.

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