Ultrasound Image Denoising And The Respiratory Signal Capture And Modeling In The Ultrasonic Surgical Navigation System

Surgical navigation system has become the current research focuses in areas of both computer technology and medical technology. In this paper we study some key technologies of ultrasound image-guided percutaneous surgical navigation system. As we all know, ultrasound imaging process is usually accompanied with severe speckle noise. The interference on the image detail caused by the noise, will bring some difficulties on further image segmentation and edge detection, and also affect doctors diagnose and efficiency. Therefore in the process of the surgery navigation, ultrasound image denoising is the first process for image registration and segmentation. The main object of percutaneous puncture operation is the tissues and organs of human, because of the displacement of the soft tissues and organs during the human respiratory movement, the registration of CT and MR before surgery and in surgery will be affected, as well the accuracy of puncture. To solve those problems, the article studies the acquirement and modeling of ultrasound denoising and respiratory movement, the main contents are:For ultrasound image denoising problem, we propose a denoising based on the curvature of the multiplicative noise weighted total variation model algorithm. Due to curvature differences is introduced, the proposed algorithm can remove multiplicative noise while preserving image edges and detail effectively. And by using Bregman iterative methods to solve the model can obtain more satisfactory results.In this paper, we build the vitro respiratory signal detection system based on optical positioning in Visual Studio2010and OpenCV environment, and achieve a real-time detection signal, this system is able to detect three landmarks, and the detection rate is up to20fps.We obtain vivo respiratory signal from ultrasound image sequences using a template matching method, it have a good correlation with in vitro respiratory signal. This paper proposes using the Hessian matrix to get in vivo respiratory signal automatically from the ultrasound image sequences. Then use the polynomial and SVR model for vitro and vivo respiratory motion modeling, experimental results show that multiple vitro signal and vivo models is better able to establish in single vitro and vivo respiratory signal correlation model.