Abstract
At present, feature-based 3D reconstruction and tracking technology is widely applied in the medical field. In minimally invasive surgery, the surgeon can achieve three-dimensional reconstruction through the images obtained by the endoscope in the human body, restore the three-dimensional scene of the area to be operated on, and track the motion of the soft tissue surface. This enables doctors to have a clearer understanding of the location depth of the surgical area, greatly reducing the negative impact of 2D image defects and ensuring smooth operation. In this study, firstly, the 3D coordinates of each feature point are calculated by using the parameters of the parallel binocular endoscope and the spatial geometric constraints. At the same time, the discrete feature points are divided into multiple triangles using the Delaunay triangulation method. Then, the 3D coordinates of feature points and the division results of each triangle are combined to complete the 3D surface reconstruction. Combined with the feature matching method based on convolutional neural network, feature tracking is realized by calculating the three-dimensional coordinate changes of the same feature point in different frames. Finally, experiments are carried out on the endoscope image to complete the 3D surface reconstruction and feature tracking.
Highlights
As an important branch in the field of computer vision, three-dimensional reconstruction has been applied to many image processing fields, such as computer animation, virtual reality and digital media creation [1], and image processing in minimally invasive surgery is an important application field
The tracking of feature points helps to describe the movement of the tissue surface, so it can be of great help to minimally invasive surgery or future automated robotic surgery
The left and right images used for 3D curved surface reconstruction are matched with the model
Summary
As an important branch in the field of computer vision, three-dimensional reconstruction has been applied to many image processing fields, such as computer animation, virtual reality and digital media creation [1], and image processing in minimally invasive surgery is an important application field. In modern minimally invasive surgery, endoscopes are widely used to detect and treat diseases in various parts of the human body. This type of surgery is performed by introducing endoscopes and surgical equipment through natural holes or small incisions on the skin surface. Three-dimensional reconstruction provides doctors with a more intuitive three-dimensional model by processing the video sequence obtained by the stereoscopic endoscope. Such stereo perception can bring more qualitative and quantitative information about the shape of the tissue to the doctor. The tracking of feature points helps to describe the movement of the tissue surface, so it can be of great help to minimally invasive surgery or future automated robotic surgery
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