Title :
Lumen segmentation and visualization of abdominal aorta using geodesic active contours for intravascular surgical simulation
Author :
Fan Yang ; Zeng-Guang Hou ; Shao-Hua Mi ; Gui-Bin Bian ; Xiao-Liang Xie
Author_Institution :
State Key Lab. of Manage. & Control for Complex Syst., Inst. of Autom., Beijing, China
Abstract :
Percutaneous transluminal coronary angioplasty (PTCA) has been proved to be a standard solution to most cardiovascular diseases (CVDs). The surgical simulator provides the trainees a new vehicle to learn this skill much more conveniently and effectively. The blood vessel model is at the core of the virtual environment. In this paper, a robust and semi-automatic approach to segment the abdominal aorta from the computed tomography angiography (CTA) is developed. The proposed approach employs the geodesic active contours method as the main component. The edge potential map is generated by applying nonlinear mapping function. The initial contours are evolved by applyging the fast marching method. The surface information representing the vessel is extracted by the marching cubes method. This approach has been proved successful for the construction of 3-D surface model of the aorta based on the CTA series.
Keywords :
angiocardiography; blood vessels; cardiovascular system; computerised tomography; differential geometry; diseases; edge detection; feature extraction; image representation; image segmentation; medical image processing; surgery; 3D surface model; CTA series; CVD; PTCA; abdominal aorta segmentation; abdominal aorta visualization; blood vessel model; cardiovascular diseases; computed tomography angiography; edge potential map; fast marching method; geodesic active contours; intravascular surgical simulation; lumen segmentation; marching cube method; nonlinear mapping function; percutaneous transluminal coronary angioplasty; robust semiautomatic approach; surface information extraction; vessel representation; virtual environment; Active contours; Biomedical imaging; Computational modeling; Image edge detection; Image segmentation; Level set; Mathematical model;
Conference_Titel :
Robotics and Biomimetics (ROBIO), 2014 IEEE International Conference on
DOI :
10.1109/ROBIO.2014.7090690