Chang, Yen-Chi (University of California, Los Angeles), Berry-Pusey, Brittany (Crump Insitute for Molecular Imaging University of California, L), Tsao, Tsu-Chin (University of California Los Angeles), Chatziioannou, Arion (Crump Insitute for Molecular Imaging, University of California L)

Real-Time Image Processing for Locating Veins in Mouse Tails

Scheduled for presentation during the Invited session "Instrumentation and Characterization in Bio-Systems" (MoCT5), Monday, October 21, 2013, 17:00−17:20, Tent B

6th Annual Dynamic Systems and Control Conference, October 21-23, 2020, Stanford University, Munger Center, Palo Alto, CA

This information is tentative and subject to change. Compiled on April 24, 2024

Abstract

This paper develops an efficient vision-based real-time vein detection algorithm for preclinical vascular insertions. Mouse tail vein injections perform a routine but critical step in most preclinical applications. Compensating for poor manual injection stability and high skill requirements, Vascular Access System (VAS) has been developed so a trained technician can manually command the system to perform needle insertions and monitor the operation through a near-infrared camera. However, VAS’ vein detection algorithm requires much computation and is, therefore, difficult to reflect the real-time tail movement during an insertion. Furthermore, the detection performance is of-ten disturbed by tail hair and skin pigmentation. In this work, an effective noise filtering algorithm is proposed based on con-vex optimization. Effectively eliminating false-positive detections and preserving cross-sectional continuity, this algorithm provides vein detection results approximately every 200 ms at the presence of tail hair and skin pigmentation. This developed real-time tail vein detection method is able to capture the tail movement during insertion, therefore allow for the development of an automated Vascular Access System (A-VAS) for preclinical injections.