Research On Extraction Algorithm And Its Implementation For Knee Joint Cavity Model

Since the minimally invasive technology is widely used in the medical field, the traditional open surgery of knee is intended to be replaced by the knee arthroscopic surgery (KAS). Compared to the traditional surgery of knee, the KAS has been widely accepted by doctors and patients because its advantages in small trauma, less pain, faster postoperative recovery. However, it is very hard to see inside the global knee joint cavity through the image of arthroscopy because of the small holes and small arthroscopic view range. In addition, the cavity space of the knee joint is narrow and shows complex shape, which increases the difficulty of surgical operation. Thanks to the development of medical imaging technology and equipments, data acquisition for individual patient is easier, and the precision of data is higher than ever. These data can be used in pre-operative planning and intra-operative navigation through in-depth quantitative analysis. Therefore, this dissertation aims to explore an automatic extraction algorithm for the model of knee joint cavity. The research contents and methods will be described as follows:(1) Extraction of the boundary surface of static knee joint. For the three-dimensional model of knee joint reconstructed with medical image data, the axial bounding box (ABB) with octree structure is utilized to accelerate the computation of intersection test which is critical for the selection of a seed. Then the region growing method is used to extract the boundary surface of the knee joint cavity. Holes repairing and boundary smoothing are performed to gain the final boundary surfaces.(2) Three dimensional reconstruction of static knee joint cavity. A closed surface model is generated by suturing the two extracted boundary surfaces of femur and tibia. And then the loop surface segmentation method is used to the smoothing of cavity surfaces. The final cavity model can be constructed by three dimensional Boolean operations in which the space of the meniscus, cruciate ligament and cartilage are subtracted.(3) Modeling for the motion of knee joint and deformation of soft issue. Through the analysis of the motion characteristic of knee joint, we can draw the conclusion that there are two main influence factors to the cavity space of knee joint--one is the relative position of femur and tibia, the other is the deformation of cruciate ligaments. In this section, we first fit the curve equations of the contour line of the femur and tibia to calculate the positions of the tibia under rotation. Then build the Mass-Spring model for computing the deformation of cruciate ligaments which are affected mainly by the rotation of tibia.(4) Automatic extraction algorithm and its implementation for dynamic knee joint cavity. According to the motion modeling of knee joint and deformation modeling of cruciate ligament, we put forward an extraction algorithm of dynamic knee joint cavity model, and then build an experimental environment for its implementation.The main contribution of this job is proposing an extraction algorithm for knee joint cavity model both in static and dynamic states. When dynamic knee joint cavity is extracted, our algorithm considers the influence of deformation of cruciate ligament and the posture adjustment of tibia to knee joint cavity space. The proposed algorithm enables the efficient computation of the pose of tibia and deformation of cruciate ligaments in the case of knee joint movement. The extracted cavity models of knee joint can be used to the further quantitative analysis of knee joint cavity, and they are intended to be utilized to pre-operative planning and intra-operative navigation.