An Active Contour Model Based on Adaptive Threshold for Extraction of Cerebral Vascular Structures.
Cerebral vessel segmentation is essential and helpful for the clinical diagnosis and the related research. However, automatic segmentation of brain vessels remains challenging because of the variable vessel shape and high complex of vessel geometry. This study proposes a new active contour model (ACM) implemented by the level-set method for segmenting vessels from TOF-MRA data. The energy function of the new model, combining both region intensity and boundary information, is composed of two region terms, one boundary term and one penalty term. The global threshold representing the lower gray boundary of the target object by maximum intensity projection (MIP) is defined in the first-region term, and it is used to guide the segmentation of the thick vessels. In the second term, a dynamic intensity threshold is employed to extract the tiny vessels. The boundary term is used to drive the contours to evolve towards the boundaries with high gradients. The penalty term is used to avoid reinitialization of the level-set function. Experimental results on 10 clinical brain data sets demonstrate that our method is not only able to achieve better Dice Similarity Coefficient than the global threshold based method and localized hybrid level-set method but also able to extract whole cerebral vessel trees, including the thin vessels.
College of Information Science and Technology, Beijing Normal University, Beijing 100875, China; Beijing Key Laboratory of Digital Preservation and Virtual Reality for Cultural Heritage, Beijing 100875, China.
Jiaxin
J
Wang
College of Information Science and Technology, Beijing Normal University, Beijing 100875, China; Beijing Key Laboratory of Digital Preservation and Virtual Reality for Cultural Heritage, Beijing 100875, China.
Shifeng
S
Zhao
College of Information Science and Technology, Beijing Normal University, Beijing 100875, China; Beijing Key Laboratory of Digital Preservation and Virtual Reality for Cultural Heritage, Beijing 100875, China.
Zifeng
Z
Liu
College of Information Science and Technology, Beijing Normal University, Beijing 100875, China; Beijing Key Laboratory of Digital Preservation and Virtual Reality for Cultural Heritage, Beijing 100875, China.
Yun
0000-0001-5574-2325
Y
Tian
College of Information Science and Technology, Beijing Normal University, Beijing 100875, China; Beijing Key Laboratory of Digital Preservation and Virtual Reality for Cultural Heritage, Beijing 100875, China.
Fuqing
F
Duan
College of Information Science and Technology, Beijing Normal University, Beijing 100875, China; Beijing Key Laboratory of Digital Preservation and Virtual Reality for Cultural Heritage, Beijing 100875, China.
Yutong
Y
Pan
D000465:Algorithms; D001331:Automation; D001808:Blood Vessels / Q000000981:diagnostic imaging*; D001921:Brain / Q000000981:diagnostic imaging; D002560:Cerebrovascular Circulation / Q000502:physiology*; D006801:Humans; D007091:Image Processing, Computer-Assisted / Q000379:methods*; D021621:Imaging, Three-Dimensional / Q000379:methods*; D018810:Magnetic Resonance Angiography; D008490:Medical Informatics / Q000379:methods; D015233:Models, Statistical; D010363:Pattern Recognition, Automated; D015203:Reproducibility of Results; D012984:Software