A vessel contour detection and estimation method for robot assisted endovascular surgery

X-ray angiography is a common method for image-guided navigation comprising surgical devices and vessel contours detection which can offer visual feedback to physicians. However, for the sake of both physicians' and patients' health, the contrast agent is injected intermittently and in a low dosage, so the X-ray images are not always of adequate quality for visual examination. So the navigation, which is to achieve the real-time positions of vessels and devices, is a challenging task. On one hand, the vessels and devices are always affected by other tissues. On the other hand, when the contrast agent is absent, vessels are invisible, so it is impossible to detect vessel contours. Aimed at the above problems, this paper proposes a vessel detection and estimation method. The paper has two main contributions. First, the paper uses the multiple scales top-hat enhancement method to improve image contrast rate, which is benefit for the vessel contours detection. Second, the paper proposes a method to estimate the vessel contours when the contrast agent is absent, it is based on the fact that vessel moves in a small range and the vessel contours are estimated by using the contour detection results of the adjacent frames. The experiment has been tested on 18 sequences images and the average accuracy is 92.6%. The subjective evaluation and the experiment results demonstrate the effectiveness and robust of the proposed method.